Prediction of Driv er Lane-Change
Beha vior -
Mo deling, F eature Selection and
Ev aluation
v orgelegt v on
M. Eng.
Xiaohan Li
v on der F akultät V - V erk ehrs- und Masc hinensysteme
der T ec hnisc hen Univ ersität Berlin
zur Erlangung des akademisc hen Grades
Doktor der Ingenieurwissensc haften
-Dr.-Ing.-
genehmigte Dissertation
Promotionsaussc h uss:
V orsitzender: Prof. Dr. phil. Manfred Th üring
Gutac h ter: Prof. Dr.-Ing. Matthias Rötting
Gutac h ter: Prof. Dr. phil. Klaus Bengler
T ag der wissensc haftlic hen A ussprac he: 21.10.2019
Berlin 2019

Zusammenfassung
Die V orhersage v on Spurw ec hselv erhalten (SW) im motorisierten Straßen v erk ehr ist ein
wic h tiger Sc hritt zur V ermeidung v on Spurw ec hselunfällen. Ziel dieser Dissertation
ist deshalb die En t wic klung einer umfassenden Metho de zur V orhersage v on SW,
einsc hließlic h exp erimen tellen Designs, Mo dellierung, A usw ahl relev an ter F ahr- und
F ahrerInnen-V ariablen und deren Ev aluation. Die Metho de k onzen triert sic h hierb ei
auf die A utobahn als F ahrumgebung. Es wurden drei aufeinander aufbauende Studien
durc hgeführt, ein F ahrsimulatorexperiment, eine Analyse b estehender Realfahrtdaten,
so wie eine Realfahrtstudie. Hierb ei kamen Metho den des Masc hinellen Lernens (ML), der
Informatik, der Statistik, so wie Metho den der Human F actors F orsc h ung zum Einsatz.
In der ersten Studie der Dissertation wurde eine k omplexe Metho de zur V orhersage
v on SW en t wick elt, die b estehende Metho den zu SW V orhersage w eiteren t wic k elt.
In einem ersten Sc hritt wurden V erkehrsumfeldfaktoren so wie der F ahrstil v on
A utofahrerInnen gen utzt um einen F ahrstil T rainingsdatensatz zu erstellen. Zusätzlic h
wurde ein w eiterer T rainingsdatensatz ohne Ein b ezug v on V erk ehrsumfeldfaktoren und
F ahrstil erstellt, der Unkate gorisiert Datensatz. Beide Datensätze wurden gen utzt um
ein ML Mo dell zu trainieren, w elc hes SW und Spurhalte (SH) Daten klassifizieren
kann. Zusätzlic h wird eine neuartige blickbasierte Klassifikationsmethode (BKM)
en t wic k elt, um SW- und SH-Daten zu b esc hriften, und die BKM mit der b estehenden
Zeitfensterklassifikation (ZFK) v erglic hen. Die Ergebnisse zeigen, dass ML-Mo delle, die
durc h F ahrstil-Datensätze trainiert w erden, höhere Klassifizierungsw erte erzielen k önnen
als ML Mo delle die mit dem unkategorisierten Datensatz trainiert werden. Darüb er
hinaus ist die Klassifikationsleistung der Mo delle durc h die V erw endung der BKM-
Metho de vielv ersprec hender als durc h die ZFK-Metho de. Um den Einsc hränkungen der
ersten Studie, sp eziell der Datenerhebung im F ahrsim ulator und der V ariablenausw ahl
mit unzureic hender empirisc her Datenlage, en tgegenzu wirk en, wurde eine zw eite Studie
durc hgeführt, die einen b estehenden Datensatz mit Realfahrtdaten analysiert.
Das Ziel der zw eiten Studie ist die En t wic klung einer systematisc hen Metho de
zur V ariablenausw ahl, um Lüc k en in der bisherigen F orsc h ung im Zusammenhang
mit der V orhersage des SW-V erhaltens zu sc hließen. A us b estehenden Realfahrtdaten
w erden mehrere für das SW-V erhalten relev an te V ariablen extrahiert, z.B. dynamisc he
V ariablen der F ahrzeugb ew egung, des F ahrerInnen v erhaltens, k omplexere V ariablen,
die mehrere V ariablen k ombinieren, so wie Zeitfensterv ariablen, die Änderungen im

Zeitv erlauf in tegrieren. Darüb er hinaus w erden V ariablen aus dem F requenzb ereic h
extrahiert. Im Gegensatz zu b estehenden Metho den zur V ariablenausw ahl v erw endet
diese Studie statistisc he Metho den, die ein tieferes V erständnis des Beitrags des einzelnen
Merkmals zum SW-V erhalten der F ahrerInnen ermöglic hen. Dieser Ansatz ist außerdem
allgemeiner als b estehende Ansätze, die eine V ariablenausw ahl n ur für einen b estimm ten
Algorithm us erlaub en.
In einem dritten Exp erimen t w erden die Metho den aus den ersten b eiden Studien
k om biniert, um SW-V erhalten in einem Realfahrtexp eriment v orherzusagen. Ob w ohl
diese Studie ähnlic he Metho den wie die ersten b eiden Studien v erw endet, bietet sie eine
einzigartige Gelegenheit, die Mac h bark eit des v orgesc hlagenen SW-V orhersageansatzes
un ter realen Straßen b edingungen zu testen. Neb en dem F ahrstil Datensatz und
dem Unkate gorisiert Datensatz wird in dieser Studie zusätzlic h ein Personalisiert
Datensatz gen utzt, der für jede V ersuchsperson individuell angelegt wird. Es wird
ein V ergleic h der V orhersage v on SW mittels der v ersc hiedenen T rainingsdatensätze,
ML-Mo dellen und Klassifikationsmetho den, so wie der F rage, ob Eye-trac king für die
SW-V orhersage ein b ezogen w erden soll, durc hgeführt. Die Ergebnisse deuten darauf hin,
dass ML-Mo delle am b esten mit der K om bination aus der V erw endung p ersonalisierter
T rainingsdatensätze und der BKM-Metho de mit der V erw endung v on Ey e-T rac king-
Informationen funktionieren. Im absc hließenden sim ulierten T est der V orhersage in der
Realfahrt k onn te das en t wic kelte Modell das SW-V erhalten der F ahrerInnen im Mittel
3,3 s (Genauigk eit 93,5 %) v or einem tatsäc hlic hen SW-Manö v er für einen linksseitigen
SW, und 2,6 s (Genauigk eit 72,4 %) für einen rech tsseitigen SW v orhersagen.
Die in dieser Dissertation en tw ic k elte Metho de zur V orhersage v on SW-V erhalten
kann in der An w endung dazu b eitragen, die Anzahl der Spurw ec hselunfälle zu v erringern
und somit die Zahl der daraus resultierenden V erletzungen und T o desfälle reduzieren.
iv

Abstract
Prediction of driv er lane-c hange (LC) b eha vior is v ery imp ortan t to a v oid LC related
traffic acciden ts. The aim of this dissertation is to prop ose a comprehensiv e framew ork,
including exp erimen tal design, mo deling, feature selection as w ell as ev aluation, whic h
can b e implemen ted for prediction of driv er LC b eha vior. The framew ork is designed
to concen trate on high w ay roads. T o this end, three studies w ere conducted step b y
step including a driving sim ulator based exp erimen t, a big data analysis, and a real-
road exp erimen t. Metho ds used in this dissertation in v olve mac hine-learning (ML),
informatics, statistics as w ell as the h uman factor field.
In the first study of the dissertation, a complete framew ork of prediction of driv er
LC b eha vior w as dev elop ed with sev eral metho dological inno v ations in comparison to
prior researc h. Firstly , driving con textual traffic and driving st yle w ere considered for
the preparation of the training datasets, termed as driving style datasets . Datasets
without an y additional consideration w ere termed as non-c ate gorize d datasets . These
datasets w ere used to train the ML mo dels to classify LC and lane-k eep (LK) data
samples. Secondly , a newly gaze-based lab eling (GBL) metho d w as further prop osed
to lab el LC and LK data samples compared with the time-windo w lab eling (TWL)
metho d whic h w as commonly used b y the related w orks. The results sho w that ML
mo dels trained b y the driving st yle datasets can ac hiev e higher classification scores than
the non-categorized datasets. In addition, b y using the GBL metho d, the classification
p erformances of the mo dels are more promising than b y using the TWL metho d. T o
coun ter the limitations of the first study , i.e. data collection from the driving sim ulator
and the feature selection based on the insufficien t empirical kno wledge, a second study
w as conducted based on a large set of naturalistic driving data.
The aim of the second study w as to prop ose a systematic feature selection metho d
to fill in gaps of prior researc h related to prediction of driv er LC b eha vior. In order to
enric h the feature sets, a comprehensiv e set of features related to driv er LC b eha vior w ere
extracted, e.g. dynamic features of v ehicle mo v emen t, b eha vioral features of the driv er,
more complex features that com bine m ultiple v ariables, as w ell as time-windo w features
whic h in tegrate c hanges o v er time. In addition, features from the frequency domain w ere
extracted b y v arying time-windo ws. In con trast to the established metho ds for feature
selection, this study uses statistical metho ds that allo w a deep er understanding of the
con tribution of the individual feature to driv er LC b eha vior and is more generalized in

comparison to prior researc h where the feature selection metho ds tend to w ork only for
one sp ecific algorithm.
Finally , com bining the metho ds dev elop ed in the first t w o studies, a real-road
exp erimen t w as conducted to ev aluate the complete framew ork for LC prediction
prop osed in this dissertation. While this study uses similar metho ds to the first t w o
studies, it pro vides a unique opp ortunit y to assess the feasibilit y of the prop osed LC
prediction approac h under real-road conditions. Besides driving style datasets and
non-c ate gorize d datasets , in this study , p ersonalize d datasets , i.e. eac h participan t has
his/her individual dataset, w ere added for comparison. Comparison w as made b etw een
training datasets, ML mo dels, and lab eling metho ds as w ell as the comparison b et w een
fusing and without fusing ey e-trac king signals and only using ey e-trac king signals for
prediction. The result suggests that ML mo dels p erform b est with the com bination of
using the p ersonalized training datasets and the GBL metho d b y fusing ey e-trac king
signals. In the final sim ulated real-time prediction test, the mo del could predict driv er
LC b eha vior around 3.3 s (precision 93.5%) ahead of an actual LC maneuv er for left LC
case and 2.6 s (precision 72.4%) for righ t LC case.
In conclusion, the framew ork for driv er LC prediction dev elop ed in this dissertation,
if implemen ted, could help to decrease the n um b er of LC related crashes and reduce the
resulting n um b er of injuries and fatalities.
vi

DEDICA TION
T o m y paren ts
for letting me pursue m y dream
for so long
so far a w a y from home

A c kno wledgemen ts
First of all, I w ould lik e to express sincere gratitude to m y sup ervisor Prof.
Dr.-Ing. Matthias Rötting, for his con tin uous supp ort on m y Ph.D study , for
encouraging me whenev er I w as stuc k, and for guiding me to gro w as a real
researc h scien tist. I also w ould lik e to thank Prof. Dr. phil Klaus Bengler
for his willingness to sup ervise m y thesis and giving me v aluable suggestions.
My sincere thanks also go to all of m y colleagues at Chair of Human-Mac hine-
Systems for the kindly help in the last four y ears. In particular, I w ould lik e
to thank Mario Lasc h and Stefan Damk e for their tec hnical supp ort to m y
exp erimen ts, patien tly . And also for our secretary Mrs. Elisab eth Langer
who alw a ys kindly reminds me to ha v e a break when I w as w orking for to o
long. A sp ecial thanks go es to m y colleague and friend F elix Sieb ert for his
enligh tening suggestion at the b eginning of m y researc h and for the German
translation.
Beside, I w ould lik e to thank Jo yson Safet y Systems Gm bH for their
co op eration b y pro viding the exp erimen tal v ehicle for m y third study .
Last but not least, I w ould lik e to thank m y family for their endless lo v e.
ix

T able of Contents
Title P age i
Zusammenfassung iii
Abstract v
List of Figures xv
List of T ables xix
Abbreviations xxi
1 In tro duction 1
1.1 Motiv ation of the dissertation . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Structure of the dissertation . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Review - Prediction of driv er lane-c hange b eha vior 5
2 . 1 O v e r v i e w ................................... 5
2.2 Driv er lane-c hange b eha vior . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2.1 T yp es of lane-change b eha vior . . . . . . . . . . . . . . . . . . . 6
2.2.2 Lane-c hange decision-making pro cess . . . . . . . . . . . . . . . 6
2 . 3 P r e d i c t o r ................................... 8
2 . 4 P r e d i c t i o n m o d e l .............................. 1 0
2 . 5 A p p l i c a t i o n ................................. 1 2
2 . 6 S u m m a r y .................................. 1 2
3 Mathematical bac kground 13
3.1 Mac hine learning mo del . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1.1 Classification of ML mo dels . . . . . . . . . . . . . . . . . . . . 13
3 . 1 . 2 B a y e s i a n n e t w o r k .......................... 1 4
3.1.3 Gaussian mixture mo del . . . . . . . . . . . . . . . . . . . . . . . 17
3 . 1 . 4 O t h e r M L m o d e l s .......................... 2 0
3 . 2 P a r a m e t e r l e a r n i n g ............................. 2 6
3 . 3 E v a l u a t i o n m e t h o d .............................. 2 7
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3.3.1 Receiv er op erating characte ristic . . . . . . . . . . . . . . . . . . 27
3 . 3 . 2 A r e a u n d e r c u r v e ........................... 2 7
3 . 3 . 3 C r o s s - v a l i d a t i o n ............................ 2 7
3 . 4 S u m m a r y .................................. 2 8
4 Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a
driving sim ulator exp erimen t 29
4 . 1 I n t r o d u c t i o n ................................. 2 9
4 . 2 D r i v i n g s c e n a r i o ............................... 3 2
4.2.1 Mo deling con textual traffic . . . . . . . . . . . . . . . . . . . . . 32
4.2.2 F eature extraction . . . . . . . . . . . . . . . . . . . . . . . . . 35
4 . 3 E x p e r i m e n t ................................. 3 6
4.3.1 Exp erimen tal setup . . . . . . . . . . . . . . . . . . . . . . . . . 36
4 . 3 . 2 P a r t i c i p a n t s ............................. 3 8
4.3.3 Driving st yle classification . . . . . . . . . . . . . . . . . . . . . 40
4.4 Lane-c hange data lab eling . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4.1 Gaze-based lab eling metho d . . . . . . . . . . . . . . . . . . . . 42
4.4.2 Time-windo w lab eling metho d . . . . . . . . . . . . . . . . . . . 44
4.5 Mo del implemen tation . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4 . 5 . 1 B a y e s i a n n e t w o r k .......................... 4 4
4.5.2 Other mac hine learning mo dels . . . . . . . . . . . . . . . . . . 49
4.5.3 Mo del training and ev aluation metho d . . . . . . . . . . . . . . 50
4 . 6 R e s u l t a n d a n a l y s i s .............................. 5 1
4.6.1 Comparison b et w een differen t mo dels . . . . . . . . . . . . . . . 52
4.6.2 Comparison b et w een training datasets . . . . . . . . . . . . . . 53
4.6.3 Comparison b et w een the lab eling metho ds . . . . . . . . . . . . 54
4.6.4 Real-time lane-c hange b ehavior prediction . . . . . . . . . . . . 54
4 . 7 S u m m a r y ................................... 5 7
5 Big data analysis - Ev aluation of feature selection for driv er lane-
c hange b eha vior 59
5 . 1 I n t r o d u c t i o n ................................. 5 9
5.2 Related w ork of feature selection . . . . . . . . . . . . . . . . . . . . . . 61
5.3 Lane-c hange scenario mo deling . . . . . . . . . . . . . . . . . . . . . . 62
5.4 Data pro cessing and feature extraction . . . . . . . . . . . . . . . . . . 66
5.4.1 Naturalistic driving data . . . . . . . . . . . . . . . . . . . . . . 66
5.4.2 F eature extraction . . . . . . . . . . . . . . . . . . . . . . . . . 69
5 . 4 . 3 D a t a l a b e l i n g ............................ 7 3
5 . 5 E v a l u a t i o n m e t h o d ............................. 7 3
5.5.1 F eature ev aluation . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.5.2 Mo dels used for feature ev aluation . . . . . . . . . . . . . . . . 74
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5 . 6 R e s u l t a n d a n a l y s i s ............................. 7 5
5.6.1 Analysis on effect size and p - v a l u e ................. 7 5
5.6.2 Final selected features for eac h LC scenario . . . . . . . . . . . 76
5.6.3
Ev aluation of differen t mac hine learning mo dels using the selected
f e a t u r e s ............................... 7 9
5 . 7 S u m m a r y ................................... 8 1
6 Exp erimen t 2 - Ev aluation based on a real-road exp erimen t 83
6 . 1 I n t r o d u c t i o n ................................. 8 3
6 . 2 E x p e r i m e n t a l d e s i g n ............................ 8 3
6 . 2 . 1 E q u i p m e n t .............................. 8 3
6 . 2 . 2 P a r t i c i p a n t s ............................. 8 5
6 . 2 . 3 D r i v i n g t a s k .............................. 8 7
6 . 3 D a t a p r o c e s s i n g............................... 8 9
6.3.1 Ey e-trac king data pro cessing . . . . . . . . . . . . . . . . . . . . 90
6.3.2 P arsing CAN data . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.3.3 F eature extraction . . . . . . . . . . . . . . . . . . . . . . . . . 95
6 . 4 M e t h o d ................................... 9 6
6.4.1 Lab eling lane-c hange dataset . . . . . . . . . . . . . . . . . . . . 96
6.4.2 F eature selection . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6 . 4 . 3 T r a i n i n g d a t a s e t .......................... 1 0 3
6 . 5 E v a l u a t i o n r e s u l t .............................. 1 0 6
6.5.1 Mo del and dataset comparison . . . . . . . . . . . . . . . . . . . 106
6.5.2 Lab eling metho d comparison . . . . . . . . . . . . . . . . . . . . . 107
6.5.3 Real-time p erformance ev aluation . . . . . . . . . . . . . . . . . 108
6 . 6 S u m m a r y .................................. 1 1 3
7 Discussion and outlo ok 115
7 . 1 O v e r a l l c o n c l u s i o n .............................. 1 1 5
7 . 1 . 1 M o d e l i n g .............................. 1 1 5
7 . 1 . 2 F e a t u r e s e l e c t i o n ........................... 1 1 7
7 . 1 . 3 E v a l u a t i o n .............................. 1 1 8
7 . 1 . 4 C o n t r i b u t i o n ............................ 1 2 0
7 . 2 O u t l o o k .................................... 1 2 1
App endix A Exp erimen t 1 123
A . 1 D o c u m e n t s ................................. 1 2 3
A.1.1 Demographic questionnaire - German v ersion . . . . . . . . . . 123
A.1.2 Beha vioral-psyc hological questionnaire - German v ersion . . . . . 127
A.1.3 Ov erall instruction on the participan ts - German v ersion . . . . 130
A.1.4 Exp erimen t instruction - German v ersion . . . . . . . . . . . . . 132
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A . 2 F i g u r e s .................................... 1 3 4
A.2.1 Statistics of the demographic questionnaire . . . . . . . . . . . . 134
A.2.2 Result of mo del comparison . . . . . . . . . . . . . . . . . . . . 135
A.2.3 Result of lab eling metho d comparison . . . . . . . . . . . . . . . . 137
A . 3 T a b l e s .................................... 1 3 9
A.3.1 F ull scale of A UC v alues b y LCBN-GMM using TWL metho d . 139
App endix B Big data analysis 141
B . 1 T a b l e s ..................................... 1 4 1
B.1.1 Result of feature selection for LLC scenarios . . . . . . . . . . . . 141
B.1.2 Result of feature selection for RLC scenarios . . . . . . . . . . . 145
B . 2 F i g u r e s .................................... 1 4 8
B.2.1
Result of mo del p erformance using selected features for LLC
s c e n a r i o s ............................... 1 4 8
B.2.2
Result of mo del p erformance using selected features for RLC
s c e n a r i o s ................................ 1 5 1
App endix C Exp erimen t 2 153
C . 1 D o c u m e n t s ................................. 1 5 3
C.1.1 Driv er selection questionnaire - German v ersion . . . . . . . . . 153
C.1.2 Recruiting participan ts - German version . . . . . . . . . . . . . 155
C.1.3
Demographic questionnaire - The graphical user in terface in German
158
C.1.4 Exp erimen tal instruction - German v ersion . . . . . . . . . . . . 160
C . 2 F i g u r e s .................................... 1 6 5
C.2.1 Statistics of the demographic questionnaire . . . . . . . . . . . . 165
C . 2 . 2 D a t a s a m p l e ............................. 1 6 6
C.2.3 Mo del and dataset comparison . . . . . . . . . . . . . . . . . . . . 167
C.2.4 Lab eling metho d comparison . . . . . . . . . . . . . . . . . . . . 168
C . 3 T a b l e s .................................... 1 6 9
C.3.1 Extracted features . . . . . . . . . . . . . . . . . . . . . . . . . 169
C.3.2 Result of feature selection . . . . . . . . . . . . . . . . . . . . . 172
References 183
xiv

List of Figures
2.1
The use case of prediction of driv er LC Beha vior. Picture extracted
f r o m A u d i ( 2 0 1 3 ) . ............................. 6
2.2 An example of the t w o t yp es of LC. . . . . . . . . . . . . . . . . . . . . . 7
2.3
The A oIs of the driv er while driving. Picture extracted from Doshi and
T r i v e d i ( 2 0 0 9 ) . ................................ 1 0
3.1
The Ba y esian net work of rain prediction with probabilit y tables (
R
=
Rain, S = Sprinkler, H = Humidit y high, T = T rue and F = F alse). . 15
3.2 The Ba y esian net w ork for data classification problem. . . . . . . . . . . . 17
3.3 An example of data classification problem. . . . . . . . . . . . . . . . . . 17
3.4 Fitting p ( x | Q = 1) with Gaussian distribution. . . . . . . . . . . . . . . 19
3.5
Fitting
p
(
x | Q
= 1) with GMM b y differen t n um b ers of mixture comp onen ts.
20
3.6 The classification result of using GMM with 3 mixture comp onen ts. . . 20
3.7 An example of ho w SVM w orks to classify data from t w o classes. . . . . . 21
3.8
Using k ernel function to transform data from one space to another, where
the h yp erplane used for classification can b e describ ed b y a linear function.
22
3.9 An example of ho w KNN w orks for classification of new data. . . . . . 24
3.10 The decision tree of Whether to play tennis? . ............... 2 5
4.1
Illustration of the defined LLC scenarios, (a) Scenario lead only (b)
Scenario lead + adjacen t b ehind and (c) Scenario lead +2 adjacen t. . . 33
4.2 Illustration of the o ccupancy grid on a t w o-lane high w a y . . . . . . . . . 34
4.3 Illustration of the parameters regarding to the features. . . . . . . . . . 36
4.4 A participan t is doing exp erimen t on the sim ulator. . . . . . . . . . . . . 37
4 . 5 A p i c t u r e o f S M I E T G . ............................ 3 7
4.6 The A oIs of the driv er in a frame using BeGaze 3.6. . . . . . . . . . . . 38
4.7 Illustration of the driving habits of the participan ts in histogram. . . . 39
4.8 A screen shot of driving sim ulator and driving scenarios. . . . . . . . . 39
4.9 Illustration of the aggressiv eness scores of the participan ts in histogram. 40
4.10 The k ey momen ts during a lane c hange course. . . . . . . . . . . . . . . . 41
4.11 Lab eling LC and LK data samples to attain balanced datasets. . . . . . 43
4.12 Illustration of the TWL metho d. . . . . . . . . . . . . . . . . . . . . . 44
xv

LIST OF FIGURES
4.13
Illustration of the lane-c hange Ba y esian net w ork, where no de
X
and
Y
represen t the dynamic driving situation. . . . . . . . . . . . . . . . . . 45
4.14 Illustration of LCBN incorp orated with GMM. . . . . . . . . . . . . . . . 47
4.15 BIC v alues of three driving st yles with resp ect to parameter M . . . . . 49
4.16
The b o x plots of the lab eled momen t
t pr epar e
b efore
t 0
for differen t scenarios
and differen t lev els of aggressiv e driving st yles. . . . . . . . . . . . . . . . 51
4.17 The b o x plot of correctly predicted LC b efore t 0 . ............. 5 6
4.18 An example of the real-time prediction p erformance of LCBN-GMM. . 56
5.1 An example of LC with t w o differen t purp oses. . . . . . . . . . . . . . . 60
5.2
Illustration of the parameters used for calculating the length of the cell-grid.
62
5.3 Illustration of the mo deled LC scenarios using the cell-grid metho d. . . 65
5.4
The exp erimen tal route of the SPMD pro ject. Picture extracted
from Bezzina and Sa y er (2014). . . . . . . . . . . . . . . . . . . . . . . 66
5.5
An example of the join ted datasets of DataF r ontT ar gets , DataL ane and
DataWsu u s i n g M y S Q L . ........................... 6 7
5.6 Illustration of the time-windo w feature and the frequency domain feature. 71
5.7 Lab eling for LC and LK datasets. . . . . . . . . . . . . . . . . . . . . . 73
6.1 CAN bus setup in the testing v ehicle. . . . . . . . . . . . . . . . . . . . 84
6.2
The first p erson view from the participan t who w ears the SMI glasses is
driving on the high w a y , where the blue p oin t is the fixation monitored b y
e y e - t r a c k e r . ................................. 8 4
6.3 Data sync hronization b et w een CAN and the ey e-trac k er. . . . . . . . . 86
6.4 Illustration of the aggressiv eness scores of the participan ts in histogram. 86
6.5
The pie c hart of classification of driving styles based on scores, where
high, medium and lo w represen ts the lev els of aggressiv e driving st yles. . 87
6.6
The 3-b y-3 matrix card used for calibration of the ey e-trac k er using, where
the red p oin t is the fixation p oin t of the participan t. . . . . . . . . . . . 88
6.7
A screen shot of go ogle map whic h captures the route (in clo c kwise) of
the exp erimen t in Berlin. . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.8 The definition of the 7-region A oIs in soft w are BeGaze 3.7. . . . . . . . . 91
6.9
A screen shot represen ts the seman tic gaze ev en ts in soft w are BeGaze 3.7.
92
6.10
A reference frame whic h illustrates the gaze map hitting on A oIs, where
the term L Window refers to L eft window and R Window refers to R ight
window . ................................... 9 3
6.11 An example of ho w linear in terp olation w orks. . . . . . . . . . . . . . . 94
6.12 Illustration of the motion of the ego v ehicle. . . . . . . . . . . . . . . . 96
6.13
The k eyb oard whic h is used for man ually lab eling lane-change ev en ts
o n - b o a r d . ................................... 9 7
6.14 Illustration of the selected time of the on-b oard lab eling task. . . . . . . 97
xvi

LIST OF FIGURES
6.15 Demonstration of using the GBL metho d to lab el LC and LK datasets. 99
6.16 The b o x plot of t pr epar e ahead of t 0 . .................... 1 0 0
6.17 Demonstration of using the TWL metho d for b oth LLC and RLC case. . 101
6.18 Non-zero histogram of mirror glancing ration for LLC and RLC scenario. 109
6.19 Illustration of TP , FN and FP of the real-time prediction. . . . . . . . . 111
6.20 An example of the real-time prediction of LC b y SVM. . . . . . . . . . 113
A.1
Illustration of the bac kground information of the participan ts in histogram.
134
A.2
The R OC curv es of LCBN-GMM, Naiv e Bay es, and SVM in Scenario
l e a d o n l y . .................................. 1 3 5
A.3
The R OC curv es of LCBN-GMM, Naiv e Bay es, and SVM in Scenario
l e a d + a d j a c e n t b e h i n d . .......................... 1 3 5
A.4
The R OC curv es of LCBN-GMM, Naiv e Bay es, and SVM in Scenario
l e a d + 2 a d j a c e n t . .............................. 1 3 6
A.5
The R OC curv es of using differen t lab eling strategies in Scenario lead only .
137
A.6 The R OC curv es of using differen t lab eling strategies in Scenario lead +
a d j a c e n t b e h i n d . ............................... 1 3 7
A.7
The R OC curv es of using differen t lab eling strategies in Scenario lead +2
a d j a c e n t . .................................. 1 3 8
B.1 The R OC curv es of the classification p erformance for LLC Scenario 0_0. 148
B.2 The R OC curv es of the classification p erformance for LLC Scenario 0_1. 149
B.3 The R OC curv es of the classification p erformance for LLC Scenario 1_0. 149
B.4 The R OC curv es of the classification p erformance for LLC Scenario 1_1. 150
B.5 The R OC curv es of the classification p erformance for RLC Scenario 0_0. 151
B.6 The R OC curv es of classification p erformance for RLC Scenario 0_1. . . 151
B.7 The R OC curv es of the classification p erformance for RLC Scenario 1_0. 152
B.8 The R OC curv es of the classification p erformance for RLC Scenario 1_1. 152
C.1 The statistics of the demographical questionnaire in histogram. . . . . . 165
C.2
The output data example from soft w are BeGaze 3.7, where the term
white scr e en refers to Wind scr e en and L Window refers to L eft mirr or . . 166
C.3
The classification p erformance of LCBN-GMM, SVM and NB trained b y
differen t datasets using GBL metho d. . . . . . . . . . . . . . . . . . . . . 167
C.4
The classification p erformance of LCBN-GMM and SVM trained by t w o
differen t datasets using TWL metho d. . . . . . . . . . . . . . . . . . . 168
xvii

List of T ables
4.1 The n um b er of lab eled data samples using the GBL metho d. . . . . . . 50
4.2
The mean and SD (in second) of the lab eled
t pr epar e
b efore momen t
t 0
for
differen t scenarios and driving st yles. . . . . . . . . . . . . . . . . . . . 52
4.3 The A UC v alues p erformed b y differen t mo dels with GBL. . . . . . . . 52
4.4
The mean and SD of the A UC v alues p erformed b y LCBN-GMM with
differen t lab eling metho ds. . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.5
The real-time prediction result p erformed b y LCBN-GMM using GBL
a n d T W L m e t h o d . ............................. 5 5
5.1 The reference v alues regarding to the parameters for the cell grid . . . 64
5.2 The description of datasets DataF r ontT ar gets .............. 6 8
5.3 The description of datasets DataL ane ................... 6 8
5.4 The description of datasets DataWsu ................... 6 9
5.5 The otal amoun t of LC cases . . . . . . . . . . . . . . . . . . . . . . . . 69
5.6 Description of the extracted features. . . . . . . . . . . . . . . . . . . . 72
5.7 The final selected strong features of eac h LLC scenario. . . . . . . . . . . 77
5.8 The final selected strong features of eac h RLC scenario. . . . . . . . . . 78
5.9
The A UC v alues of the classification results b y differen t mo dels using the
selected features and all features in LLC scenarios. . . . . . . . . . . . 80
5.10
The A UC v alues of the classification results b y differen t mo dels using the
selected features and all features in RLC scenarios. . . . . . . . . . . . 80
6.1 Illustration of the parsed CAN signals. . . . . . . . . . . . . . . . . . . 94
6.2 The statistics of LC cases in the real-road exp erimen t. . . . . . . . . . 98
6.3 The lab eled training samples b y the GBL and TWL metho d. . . . . . . 102
6.4
The feature selection results of differen t lab eling metho ds for LLC scenario.
104
6.5
The feature selection results of differen t lab eling metho ds for RLC scenario.
105
6.6
The A UC v alues of LCBN-GMM, SVM and NB trained b y differen t
datasets using GBL metho d . . . . . . . . . . . . . . . . . . . . . . . . 106
6.7
The A UC v alues of LCBN-GMM trained b y differen t datasets using b oth
G B L a n d T W L m e t h o d . ........................... 1 0 7
xix

LIST OF T ABLES
6.8
The A UC v alues of SVM trained b y differen t datasets using b oth GBL
a n d T W L m e t h o d . ............................. 1 0 8
6.9 The real-time LC prediction result p erformed b y LCBN-GMM and SVM. 111
A.1
The A UC v alues of LCBN-GMM using TWL metho d in differen t time-
w i n d o w s i z e . ................................. 1 3 9
B.1 The full scale effect size of the features in LLC scenarios. . . . . . . . . . 141
B.2 The full scale effect size of the features in RLC scenarios. . . . . . . . . 145
C.1 Description of the extracted features from real road exp erimen t. . . . . 169
C.2 The full scale effect size of the extracted feature for LLC case. . . . . . 172
C.3 The full scale effect size of the extracted feature for RLC case. . . . . . . 177
xx

Abb reviations
AD AS adv anced driv er assistance systems 1
ANN Artificial neural net w orks 11
A oI Area of interest 9
A UC Area under curve 27
BIC Ba y esian information criterion 49
BN Ba y esian netw orks 11
CAN Con troller Area Net work 8
CV Cross-v alidation 28
DLC Discretionary lane-c hange 6
DT Decision tree 11
EM Exp ectation-maximization 26
FFT F ast F ourier transform 71
FPR false p ositiv e rate 27
GBL Gaze-based lab eling metho d 32
GMM Gaussian mixture mo del 18
HMM Hidden Mark o v mo del 11
KNN k-nearest neigh b or 11
LC lane-c hange 1
LK lane-k eep 31
LLC Left lane-c hange 32
ML Mac hine learning 11
MLC Mandatory lane-c hange 6
NB Naiv e Ba yes 11
RLC Righ t lane-c hange 64
RNN Recurren t neural net works 8
xxi

Abbreviations
R OC Receiver operating characteristic 27
SPMD Safet y pilot mo del deplo ymen t 66
SVM Supp ort v ector mac hines 11
TLC Time to line crossing 70
TPR T rue p ositiv e rate 27
TTC Time to collision 9
TW Time-windo w 71
TWL Time-windo w lab eling metho d 31
xxii

1
Intro duction
1.1 Motiv ation of the dissertation
In 2017 alone, a total of 3,180 p eople w ere killed in road traffic acciden ts in
German y (F ederalStatisticalOffice, 2017). The n um b er of high w a y fatalities in the
USA is 37,461 in 2016, whic h w as an increase of 5.6% from 2015 (NHTSA, 2016). New
data sho ws that more than 90% of traffic accidents are related to h uman error (Singh,
2015). Under suc h backdrop, v arious adv anced driv er assistance systems (AD ASs),
e.g. adaptiv e cruise con trol systems, lane departure w arning system etc., ha v e b een
dev elop ed to assist the driv er in order to increase driving safet y . Ho w ev er, researc h still
needs to b e done one step further in order to impro v e the functionalit y and reliabilit y of
AD ASs. Understanding and mo deling driv er b eha vior pro vides a solution for dev eloping
in telligen t automotiv e application (Plö c hl and Edelmann, 2007). If AD AS can predict
driv er b eha viors sev eral seconds in adv ance, it can prev en t a p oten tial acciden t from an
improp er driv er b eha vior (Mokhiamar and Ab e, 2002).
There are v arious driv er b eha viors that are necessary to b e fo cused on, e.g. predicting
driv er lane-c hange (LC) b eha vior and driv er turning b eha vior can a v oid side crashes,
braking or accelerating b eha vior for rear-end collision and detecting driv er dro wsiness
b eha vior for sev ere acciden t etc. Researc hing on all of them tak es plen t y of time. Instead,
this dissertation is fo cusing on one sp ecific driv er b eha vior comprehensiv ely rather than
doing researc h that co v ers all the asp ects but only sup erficially .
Among the ab o v e men tioned driv er b eha viors, LC b eha vior is one of the most
imp ortan t one whic h ma y lead to a sev ere traffic acciden t but it is difficult to predict.
A ctually , LC crashes accoun t for ab out 10% of all crashes (Barr and Na jm, 2001) and
1.5% of all motor v ehicle fatalities in the USA (NHTSA, 2017). Statistical studies also
sho w that LC acciden ts b ecause of h uman error are 89% (Luoma, Siv ak, and Flannagan,
1

1. In tro duction
1995). Th us, understanding and predicting driv er LC b ehavior is beneficial for the
dev elopmen t of AD AS and thus w e can reduce traffic acciden ts.
T o this end, in this dissertation w e are fo cusing on the prediction of driv er LC
b eha vior on high w a y road. The aim is to prop ose a comprehensiv e framew ork and
metho dology whic h can b e implemen ted for prediction of driv er LC b eha vior. The fields
of w orks included in this dissertation are regarding to mac hine-learning, informatics,
statistics as w ell as the h uman factor field.
1.2 Structure of the dissertation
The main w ork of this dissertation is structured in 7 c hapters. In c hapter 1, b y
placing emphasis on the imp ortance of traffic safet y , the aim and the bac kground of the
dissertation are in tro duced. Chapter 2 mak es an in tro duction ab out the k ey comp onen ts
of prediction of driv er LC b eha vior, which includes the general concept of driv er LC
b eha vior, LC t yp es, driv er decision-making pro cess, predictors of driv er LC b eha vior,
as w ell as mo dels used for prediction in the related w orks. All these asp ects la y the
foundation of the w ork presen ted in this dissertation. Chapter 3 details the mathematical
theories as w ell as the ev aluation metho ds used throughout this dissertation. Instead
of just explaining mathematical form ulas, the aim is to explain the theories in an
easy-to-understand w a y ev en for those who hav e little theoretical kno wledge.
F rom c hapter 4 on, the core studies in v olv ed in this dissertation are detailed. In
c hapter 4 w e prop ose a framew ork of prediction of driv er LC b eha vior. W orks include the
design of a driving sim ulator-based exp erimen t, feature extraction, mo deling mac hine
learning mo dels, training datasets preparation, mo del selection and ev aluation. Based
on the limitations summarized from the prior researc h, this c hapter is seeking to mak e
impro v emen ts. Sev eral impro v emen ts ha v e b een made i.e. considering driving con textual
traffic and driving st yles in preparing for training datasets, and prop osing a gaze-based
lab eling metho d (GBL) to obtain high qualit y class lab els.
Chapter 5 is related to a big data analysis on feature selection. The aim is to
o v ercome the limitation of the driving sim ulator based study b y prop osing a systematic
feature selection w ork in the p ersp ectiv e of statistics. The prop osed feature selection
metho d can b e used in general rather than for a sp ecific algorithm.
By summarizing the metho d prop osed in the last t w o studies and considering the
limitations, a real-road exp erimen t based study is detailed in c hapter 6. W orks whic h
ha v e b een done include the exp erimen tal design in the real traffic, real-road data
pro cessing, data lab eling, feature selection as w ell as the ev aluation of the ML mo dels.
In c hapter 7, a more general discussion ab out the results concluded in all the three
studies is giv en. A t the same time, the original con tributions of this dissertation are
also declared. In addition, an outlo ok is giv en regarding to the meaning of the practical
2

1.2 Structure of the dissertation
implemen tation using the metho ds prop osed in this dissertation as w ell as its p ossible
c hallenges.
Finally , the App endix lists all the imp ortan t materials regarding to the necessary
do cumen ts used in the exp erimen ts, data samples as w ell as the full scale of the results
in the form of tables and figures.
3

2
Review - Prediction of driver
lane-change b ehavio r
2.1 Ov erview
T o prev en t LC acciden ts, AD ASs ha v e b een dev elop ed to predict forthcoming driv er LC
b eha viors while driving. F or example, LC assistance system could assess the risk lev els
of maneuv ering a LC under the curren t driving situation. If the driver in tends to mak e
LC with high risk, an alarm will b e deliv ered to a v oid the p oten tial acciden ts. This
function is depicted in Figure 2.1. Comprehensiv ely understanding of driv er LC b eha vior
is v ery imp ortan t but non trivial. F ortunately , carefully partitioning the LC pro cedure
in to small segmen ts could mak e this goal ac hiev able. F rom the p erception-action lev el,
a complete LC task can b e roughly divided in to three stages: forming intent , pr ep aring
actions , and exe cuting actions . The driv er first forms the LC in ten t according to his/her
tra v eling plan and the curren t driving situation, and with suc h in ten t, pr ep ar es for taking
LC actions b y longitudinal adjustmen t (e.g., w aiting, accelerating, or decelerating),
and then exe cutes a series of LC actions suc h as lateral con trols as long as the driving
situation is acceptable (Windridge, Shaukat, and Hollnagel, 2013). T o ensure safe
driving, predicting driv er LC b eha vior as early as p ossible can lea v e enough time to
prev en t improp er LC b eha viors. In this c hapter w e will giv e an o v erview of the w ork
regarding to the prediction of driv er LC b eha vior from basic concept to metho dology
whic h will la y the foundation of the en tire w ork.
2.2 Driv er lane-c hange b eha vior
Lane-c hange is defined as the mo v ement of a v ehicle from one v ehicle lane to another
lane with con tin uing tra v el in the same direction in the new lane (J2944, 2013).
5

2. Review - Prediction of driv er lane-c hange b eha vior
I want to change
the lane to
over tak e him!
A ttention !
Danger ous !
T raf fic situation
and
driver st at e
det etion
Does the
driver
inten t to
LC ?
Danger ous
!
Alarm
yes
yes
no
no

Figure 2.1:
The use case of prediction of driv er LC Behavior. Picture extracted from A udi
(2013).
A ctually , driv er LC b eha vior can b e v arying from differen t t yp es with v arying purp oses.
Understanding driv er LC b eha vior in depth is crucial for mo deling driv er LC b eha vior
as w ell as the predictiv e mo dels.
2.2.1 T yp es of lane-c hange b eha vior
Mandatory lane-c hange (MLC)
MLC o ccurs when a driv er m ust lea v e a lane, suc h as when the lane in whic h they
are driving ends (due to a lane drop or when merging from an on-ramp), to b ypass
a blo c kage do wnstream, or to a v oid en tering and using a restricted lane. MLC can
also o ccur at the juncture of t w o or more tra v eled w a ys blending together in the same
direction (J2944, 2013). This t yp e of LC can b e depicted in Figure 2.2a.
Discretionary lane-c hange (DLC)
DLC o ccurs when a driv er c hanges to a lane p erceiv ed to offer b etter traffic conditions,
suc h as to ac hiev e desired sp eed, a v oid follo wing truc ks, a v oid merging traffic etc.
(Mathew, 2014). This t yp e of LC case is sho wn in Figure 2.2b.
2.2.2 Lane-c hange decision-making pro cess
One of the ma jor problems to understand certain driv er b eha vior is to phase the decision-
making pro cess of the b eha vior. LC decision-making pro cess is v ery complex since the
decision of a driv er to mak e LC dep ends on a n um b er of ob jectiv es, and at times LC
decision can b e c hanged. F or example, imagine a driv er is driving in the righ tmost
lane and wishes to turn righ t within 100 meters but still ha v e to mak e LC to the left
to pass a car park ed in front. Or, imagine a driv er wishes to accelerate shortly to
execute LC to o v ertak e the fron t car, but suddenly he/she finds that a fast mo ving car
6

2.2 Driv er lane-c hange b eha vior
Ego
vehicle
The road is
going to the
end, I should
change lane.
Object
vehicle

(a) Mandatory lane-c hange.
Ego
vehicle
Object
vehicle
He is too
slow , I want to
overtake him !

(b) Discretionary lane-c hange.
Figure 2.2: An example of the t w o t yp es of LC.
approac hing from b ehind on the destination lane, then he/she ma y ab ort the LC un til
it is safe to maneuv er the LC. F rom strategic lev el in the Gipps lane-c hange decision
structure (Gipps, 1986), the LC decision-making pro cess is the result of the answ ers of
a n um b er of questions:
• Is it p ossible to change lanes?
• Is it ne c essary to change lanes?
• Is it desir able to change lanes?
This pro cess of LC comprises of t w o decisions: whether the driving conditions are
satisfactory , and if not, whether an y other lane is b etter than the curren t lane. The
term driving conditions satisfactory implies that the driv er is satisfied with the driving
conditions of the curren t lane as he is able to main tain the desired sp eed. Imp ortan t
factors affecting the decision whether the driving conditions are satisfactory include the
sp eed of the driv er compared to the desired sp eed; the presence of hea vy v ehicles in
fron t and b ehind the sub ject v ehicle, if an adjacen t on ramp merges with the curren t
lane, whether the sub ject is tailgated etc. If the driving conditions are not satisfactory ,
the driv er compares the driving conditions of the curren t lane with the adjacen t lanes.
Imp ortan t factors affecting this decision include the difference b et w een the sp eed of
traffic in target lanes and the desired sp eed of the driv er, the densit y of traffic in target
7

2. Review - Prediction of driv er lane-c hange b eha vior
lanes, the relativ e sp eed with resp ect to the fron t v ehicle in the target lane, the presence
of hea vy v ehicles in target lanes ahead of the sub ject etc. (Mathew, 2014).
2.3 Predictor
Imagine w e w an t to predict if it rains in the follo wing few hours. Based on our prior
kno wledge, normally b efore it rains it w ould b e cloudy and h umid. And if it is going
to rain, w e can also observ e some animal activities lik e an ts building high an t moun ts,
lo w-flying birds, b ees and butterflies returning home, co ws gathering together and la ying
do wn etc. (Denham, 2012). These observ ations that can giv e clues to predict the rain
are termed as pr e dictor .
In order to predict driv er LC b eha vior, w e also seek the w a y to find the predictors.
Some k ey predictors regarding to driv er LC b ehavior can b e listed as follo ws:
Steering
The driv er maneuv ers the steering wheel to execute a LC, so steering is a direct pr e dictor
of driv er LC b eha vior. Steering wheel angel is a measuremen t of ho w m uc h the driv er
steers. By detecting steering angle from Con troller Area Net w ork (CAN) bus, driv er
LC b eha vior can b e mo deled using a dynamic Mark o v mo del (P en tland and Liu, 1999).
Similar w ork whic h uses steering wheel angle to mo del and predict LC b eha vior can
b e found in Kumar et al. (2013), Mandalia and Salvucci (2005), Salvucci (2004), and
McCall et al. (2007).
Throttle
Researc h found when the driv er w an ts to o v ertak e a slo w mo ving car, 5 s b efore executing
LC the driv er decelerates gradually , in order to a v oid colliding with the slo w er v ehicle.
Ho w ev er, so on after lane-c hange onset, the driv er accelerates to the o v ertaking sp eed
and main tains that sp eed through the rest of the LC (Salvucci and Liu, 2002). This
result indicates that w e can find some clues to predict driv er LC b eha vior b y monitoring
the op ening angle of the throttle in certain scenario lik e o ve rtaking. F or instance, a
recurren t neural net w ork (RNN) approac h w as implemen ted b y using throttle and brak e
data, as w ell as steering wheel angle to predict driv er short-term LC in ten tion (Xing and
Xiao, 2018). Therefore, throttle data collected from CAN bus can b e used for prediction.
T urn signal
It is quite common that the driv er uses the turn signal to indicate his/her LC in ten tion.
It w as found that the driv er turns on the signals appro ximately 1.5 s b efore the start of
a LC b eha vior (Salvucci and Liu, 2002), th us a computational driv er mo del w as used to
detect driv er LC b eha vior (Salvucci, Mandalia, et al., 2007). Other metho ds in v olv e
8

2.3 Predictor
the usage of the turn signals for LC prediction can b e found in Xu et al. (2012) and
Winner and Lueder (2005). Ho w ev er, one dra wbac k of using the turn signal as mo del
input for prediction is due to its instabilit y . Researc h found that the turn signals w ere
used only 44% of the time, with signals used more often for left lane c hanges (48%) than
for righ t lane c hanges (35%) (Lee, Olsen, Wierwille, et al., 2004). Th us, turn signal is
recommenced to b e used together with other signals.
Time to collision (TTC)
TTC is the time required for t w o v ehicles to collide if they con tin ue at their presen t
sp eeds on the same path. It is usually used to ev aluate collision risk (Kusano and Gabler,
2011). If the driv er follo ws a v ehicle with a small TTC, he/she ma y execute a LC to
o v ertak e the slo w leading v ehicle. Th us the TTC can b e regarded as a v aluable feature
to predict LC maneuv er (Kasp er et al., 2012). Using T TC as a feature, a dynamic
probabilistic driv abilit y map w as mo deled to giv e the driv er recommended acceleration
and timing to execute LC (Siv araman and T riv edi, 2014). More related w orks that use
TTC as a predictor for prediction of driv er LC b eha vior can b e listed in Liebner et al.
(2013) and P eng et al. (2015).
Ey e mo v emen t
Ey e mo v emen t w as found to b e an indicator of information gathering and therefore can
b e used to deriv e information ab out the next planned ob jectiv e of the driver (Lethaus
and Rata j, 2007). A statistical analysis sho ws that the driver spends most of his/her
gaze time b efore a LC lo oking at the curren t lane. As onset approac hes, more gaze time
is directed to the destination lane and the mirrors, since the driv er is c hec king to the side
and rear of the v ehicle to ensure safe passage (Salvucci and Liu, 2002). The p erio d of 3 -
4 s prior a LC is considered as critical phase of visual searc h to determine the feasibilit y
of the maneuv er (Beggiato et al., 2018). Based on Tijerina et al. (2005), during left
LC pro cess, the c hance of lo oking at the left mirror is 65% – 85% and the duration on
a v erage is 1.1 s. This result indicates that driv er LC b eha vior can b e an ticipated b y
observing mirror-glancing duration of the driv er. Lethaus, Baumann, et al. (2013) used
a mirror-glancing ratio during the last past seconds as the input of his mo del to predict
driv er LC b eha vior. In order to analyze gaze b eha vior in depth during LC b ehavior,
the area of in terests (A oI) of driv er gaze p erformance is divided in to sev eral areas as it
is sho wn in Figure 2.3. A ccording to Lee, Olsen, Wierwille, et al. (2004) it w as found
that the most lik ely glance lo cations w ere forw ard (probabilit y of 1.0), rear view mirror
(0.52), and left mirror (0.52). The highest link probabilit y v alue (0.34) w as b et w een the
forw ard and rear view mirror lo cations. The most likely glance locations for righ t LC
w ere forw ard (1.0), rear view mirror (0.55), and righ t mirror (0.21). The highest link
probabilit y v alue (0.60) w as b et w een the forw ard view and rear view mirror. The link
9

2. Review - Prediction of driv er lane-c hange b eha vior
v alue probabilities b et w een forw ard and righ t mirror and b et w een forw ard and righ t
blind sp ot w ere also relativ ely high at 0.12.
Figure 2.3:
The A oIs of the driver while driving. Picture extracted from Doshi and
T riv edi (2009).
These w orks indicate that driver gaze behavior is closely related to LC b eha vior,
ho w ev er, the c hallenge of using ey e mo v emen t signals for real-time application is ho w to
ensure trac king accuracy since gaze trac king is sensitiv e to ligh ting condition c hange (Zh u,
F ujim ura, and Ji, 2002).
Head p ose
While driving, driv er gaze b eha vior is closely related to his/her head p ose, so head p ose
can b e also regarded as a predictor of driv er LC b eha vior. Unlik e ey e mo vemen t whic h
can b e monitored b y v arious ey e-trac king devices, the metho d of estimating head p ose is
usually camera-based image pro cessing (Martin et al., 2012), whic h p oses a c hallenge to
the accuracy of head dynamic estimation tec hnique. Once robust mono cular in-v ehicle
head p ose estimation systems ha v e b een dev elop ed (Murph y-Ch utorian, Doshi, and
T riv edi, 2007; Zh u and F ujim ura, 2004), head p ose th us can b e used to mo del driv er LC
b eha vior. Doshi and T riv edi (2009) compared the metho d of using driver gaze p osition
and head p ose to predict driv er LC b eha vior, and the result found that head p ose can
ac hiev e earlier LC prediction. How ev er, a limitation of the head trac king system is that
it frequen tly loses trac k of the head direction at fast mo v emen ts. It o ccurs when the
driv er is lo oking o v er his shoulder to c hec k for bicycles (Liebner et al., 2013). In addition,
head p ose estimation is done b y computer vision tec hnology; the computational demand
is high esp ecially for v ehicle on-b oard real-time application.
2.4 Prediction mo del
No w w e go bac k to the example of w eather prediction. In the ancien t time, p eople
forecasted the w eather based on their prior kno wledge. The prior kno wledge men tioned
in the last section, i.e. b y observing animal activities, is actually based on statistics.
Because those activities frequen tly happ en, p eople can learn suc h lessons from the past
10

2.4 Prediction mo del
and then could forecast the w eather. It is the same w a y for predictiv e mo del. Predictiv e
mo del uses statistics to predict future ev en ts. Thanks to the dev elopmen t of informativ e
tec hnology , mac hine learning (ML) b ecomes a p o w erful to ol for prediction mo deling.
Among v arious ML mo dels, sup ervised learning mo dels are the most p opular to ols,
e.g. supp ort v ector mac hines (SVM), Naiv e Ba y es (NB), decision tree (DT), k-nearest
neigh b or (KNN), artificial neural net w orks (ANN), Ba y esian net w orks (BN), hidden
Mark o v mo del (HMM) etc. A n um b er of studies regarding to prediction of driv er LC
b eha vior ha v e b een carried out using sup ervised learning mo dels as follo ws:
SVM
Kumar et al. (2013) prop osed a solution to LC prediction based on the com bination of
a m ulti-class SVM classifier and Ba y esian filtering using driv er steering wheel angle and
v ehicle lane p osition data. The algorithm can predict on a v erage 1.3 seconds b efore a
LC o ccurs. The limitation is that only t w o driv ers to ok part in the exp erimen t, thus
tests with more participan ts should b e done to ev aluate the metho d. Salvucci (2004)
and Mandalia and Salvucci (2005) used a no v el mind-tr acking tec hnique incorp orated
with SVM metho d to detect LC.
Ba y esian mo del
A sparse Ba y esian learning metho dology w as prop osed b y McCall et al. (2007) to infer
driv er LC in ten tion b y estimating the head p ose of the driv er and trac king the lane mark.
It w as found that b y fusing driv er state information it can predict driv er LC b eha vior 3.0
s b efore an actual LC maneuv er. Ho w ev er, the metho d used in the pap er suffered from
harsh ligh ting conditions, hea vy traffic, o cclusion of the lane markings and extremely
p o or road conditions. Kasp er et al. (2012) in tro duced an ob ject-orien ted Ba y esian
net w orks to detect driv er maneuv ers on high w a y roads. Besides driv er LC b eha vior, this
mo del can also detect 26 other driv er b eha viors. But due to the parametrization of the
net w ork is computationally demand, a learning algorithm should b e emplo y ed.
HMM
P en tland and Liu (1999) prop osed a HMM using vehicle v elo cit y , driv er steering angle,
brak e p edal p osition as w ell accelerating p edal p osition signals to predict driv er LC
b eha vior based on a driving sim ulator exp erimen t. By giving the driv er text commands,
the mo del can recognize LC maneuv er 2 seconds after the onset of a LC command with
an accuracy of 93.3%. The limitation of this w ork is that the exp erimen t w as conducted
b y giving text commands to the participan ts rather than letting them mak e LC based
on their o wn preference. An autoregressiv e input-output HMM metho d w as prop osed
b y Jain, K oppula, Ragha v an, et al. (2015). This mo del can estimate the head p ose of
the driv er b y trac king his/her face orientation to predict LC b eha vior.
11

2. Review - Prediction of driv er lane-c hange b eha vior
Neural net w orks
P eng et al. (2015) dev elop ed a m ulti-parameter predictiv e mo del with a neural net w ork
mo del. V ehicle motion state, handing c haracteristics, driving conditions as w ell as head
mo v emen t data w ere all in v olv ed in the mo del. A v ariation of neural net w ork, termed
as recurren t neural net w orks (RNN), w as prop osed b y Jain, K oppula, Soh, et al. (2016)
to predict driv er b eha viors. Using this mo del, maneuv ers can b e an ticipated 3.5 seconds
b efore they o ccur in real-time with a precision of 90.5%.
2.5 Application
The application of predicting driv er LC b eha vior in AD AS has b een dev elop ed and
in tegrated in to a h uman-mac hine in terface to reduce driv er w orkloads and enhance
traffic safet y with either activ e or passiv e feedbac k.
A ctiv e feedbac k
An activ e feedbac k is rep orted if a LC b eha vior is feasible, then the w ell-designed driv er
assistance system will co op erativ ely assist the driv er c hanging lanes with recommended
acceleration and sp eed (Siv araman and T riv edi, 2014; Butak o v and Ioannou, 2015).
P assiv e feedbac k
P assiv e feedbac k will display LC safet y states or deliv er alarms to the driv er when
c hanging lanes is infeasible (Sc h ub ert, Sc h ulze, and W anielik, 2010; Jain, K oppula,
Ragha v an, et al., 2015).
2.6 Summary
This c hapter giv es an o v erview of the k ey comp onen ts of the prediction of driv er LC
b eha vior, including the general concept of driv er LC b eha vior, LC t yp es, driv er decision-
making pro cesses, predictors of driv er LC b eha vior, as w ell as mo dels used for prediction
in the related w orks. These con ten ts are ve ry imp ortan t items whic h are the bac kb one
of this field of researc h. All the w ork prop osed in this dissertation is based on prior
researc h, but not limited b y them.
12

3
Mathematical background
This c hapter details the mathematical theories as w ell as the ev aluation metho ds used
throughout this dissertation. Instead of just explaining mathematical form ulas, w e tend
to use liv ely examples in order to make the theory to be understo o d easily ev en for
those who ha v e little theoretical kno wledge. The detail of ho w to use these theories in
practice will b e mainly explained in the follo wing sections.
3.1 Mac hine learning mo del
In the last c hapter, w e ha v e review ed the related w orks of ho w mac hine learning (ML)
mo dels ha v e b een implemen ted to solv e the problem regarding to prediction of driv er
LC b eha vior. This section giv es an insigh t in to the math b ehind the ML mo dels whic h
w ould b e mainly used in the follo wing c hapters.
3.1.1 Classification of ML mo dels
When it comes to mac hine learning mo dels, there are t w o main t yp es of tasks: sup ervised,
and unsup ervised. The main difference b et w een the t w o t yp es is that sup ervised learning
is done using a ground truth (class lab els), or in other w ords, the prior kno wledge of what
the output v alues for our samples should b e. Therefore, the goal of sup ervised learning is
to learn a function that, giv en a set of data and desired outputs, b est appro ximates the
relationship b et w een input and output observ ables in the data. Unsup ervised learning,
on the other hand, do es not ha v e lab eled outputs, so its goal is to infer the natural
structure within a set of data p oin ts
1
. Esp ecially in our case, since w e use pr e dictors for
prediction, the ML mo dels used in this dissertation b elong to sup ervised learning.
1
More details can b e found in this link: h ttps://to w ardsdatascience.com/sup ervised-vs-unsup ervised-
learning-14f68e32ea8d, visited on 31.07.2019
13

3. Mathematical bac kground
The t w o main branc hes of sup ervised learning mo dels are generic mo dels and
discriminan t mo dels. In the case of classification problems, generic mo dels tend to mo del
ho w the training data are generated and then try to find the prop erties based on the
prior assumption. On the con trary , discriminan t mo dels do not care ab out how the
training data are generated and simply try to find the b est classification b oundary to
classify data samples. Both generic mo dels (Ba y esian net wor k, naiv e Ba y es etc.) and
discriminan t mo dels (SVM, decision tree etc.) are applied in this dissertation.
3.1.2 Ba y esian net w ork
Ba y esian net w orks are graphical structures for represen ting the probabilistic relationships
among a large n um b er of v ariables and doing probabilistic inference with those
v ariables (Neap olitan et al., 2004). In order to ha v e a b etter understanding of ho w
BN predicts ev en ts, w e use again the example of rain prediction. In c hapter 2 w e ha v e
learned that in order to predict the rain, w e need to find some pr e dictors . No w w e tak e
t w o predictors i.e. Sprinkler and Humidity for instance. Figure 3.1 depicts a famous
BN structure for the rain prediction adapted from R ussell and Norvig (1995). Based on
our exp erience w e kno w that b efore it rains, the h umidit y in the air is high. But when
the sprinkler w aters, it could also increase h umidit y . Also, the rain has a direct effect
on the use of the sprinkler (considering that when it is going to rain, the sprinkler is
usually not turned on). Then this situation can b e mo deled with a BN. All the three
v ariables are binary v alues; the joint pr ob ability of this BN can b e giv en as based on the
c hain rule (Sc h um, 2001):
P ( H , S, R ) = P ( H | S, R ) · P ( S | R ) · P ( R ) , (3.1)
where H, S and R are short for Humidit y high, Sprinkler use and Rain, resp ectiv ely .
Assume it is in a dry p erio d whic h mak es us b eliev e that it is less lik ely to rain.
So it is reasonable to set the probabilit y of rain as 0.2 and fair w eather as 0.8. In
Ba y esian inference theory it is called prior , whic h describ es one’s b eliefs b efore some
evidence is tak en in to accoun t. The probabilit y tables in Figure 3.1 regarding to sprinkler
and h umidit y are called c onditional pr ob ability . If w e observe high h umidit y in air,
then w e can compute the probabilit y of raining giv en high h umidit y condition, that is
P ( R = T | H = T) , whic h is called p osterior :
P ( R = T | H = T) = P ( R = T , H = T)
P ( H = T) , (3.2)
where T is for true.
14

3.1 Mac hine learning mo del
Sprinkler Rain
Humidity
high
P ( R= T) P ( R= F)
0.2 0.8
R P ( S= T) P ( S= F)
T 0.01 0.99
F 0.4 0.6
S R P ( H= T) P ( H= F)
F F 0.0 1.0
F T 0.8 0.2
T F 0.9 0.1
T T 0.99 0.01
Figure 3.1:
The Ba yesian net w ork of rain prediction with probabilit y tables (
R
= Rain,
S = Sprinkler, H = Humidit y high, T = T rue and F = F alse).
Using a v ariation of total probabilit y la w 2 , equation (3.2) can b e calculated as:
P ( R = T , H = T)
P ( H = T) = ∑ S P ( H = T , R = T , S )
∑ S,R P ( H = T , S, R ) . (3.3)
Com bined with BN join t probabilit y equation
(3.1)
,
P
(
H
= T
, R
= T
, S
= T) can
b e written as:
P ( H = T , R = T , S = T) = P ( H = T | S = T , R = T) · P ( S = T | R = T) · P ( R = T)
= 0 . 99 × 0 . 01 × 0 . 2=0 . 00198 ,
(3.4)
and P ( H = T , R = T , S = F) can b e written as:
P ( H = T , R = T , S = F) = P ( H = T | S = F , R = T) · P ( S = F | R = T) · P ( R = T)
= 0 . 8 × 0 . 99 × 0 . 2=0 . 1584 .
(3.5)
2
F or an y giv en even t A, the probabilit y of A can b e written as :
P
(
A
) =
∑ B P
(
A, B
) , where
∑ B
means all the p ossibilities of ev en t B.
15

3. Mathematical bac kground
So, ∑ S P ( H = T , R = T , S ) can b e written as:
∑
S
P ( H = T , R = T , S ) = P ( H = T , R = T , S = T) + P ( H = T , R = T , S = F)
= 0 . 00198 + 0 . 1584 = 0 . 16038 .
(3.6)
In the same w a y , w e can also calculate ∑ S,R P ( H = T , S, R ) :
∑
S,R
P ( H = T , S, R )=0 . 44838 . (3.7)
Com bining equation
(3.2)
,
(3.3)
,
(3.6)
and
(3.7)
w e can get the probabilit y of raining
giv en high h umidit y:
P ( R = T | H = T) = ∑ S P ( H = T , R = T , S )
∑ S,R P ( H = T , S, R )
= 0 . 16038
0 . 44838
≈ 35 . 77 % .
(3.8)
F rom this example w e kno w that b y only observing high h umidit y in air, the c hance
of raining is 35.77 %, whic h means it is less lik ely to rain. One main reason is that
the prior probabilit y of rain is lo w (
P
(
R
=T )=0
.
2 ). A ctually in BN, the p osterior
probabilit y , to a great exten t, is go verned b y the prior. If in the example w e preset the
prior of raining as 0.8 rather than 0.2, the outcome of p osterior probabilit y w ould b e
a m uc h higher v alue. Th us, the prior kno wledge is imp ortan t for Ba y esian probabilit y
inference.
The example of w eather prediction only giv es a discrete case of BN (all the v ariables
are discrete with binary states), ho w ev er, BN can also b e implemen ted with con tin uous
v ariables. Eac h v ariable in a BN is called a no de . In Figure 3.1, Sprinkler , R ain and
Humidity high are the three no des of the BN. Based on the causalit y of the three
no des, the no de R ain is termed as the paren t no de of Sprinkler and Humidity high , and
Sprinkler is the paren t no de of Humidity high . F or an y giv en BN, the join t probabilit y
of the BN can b e written as:
P ( Z 1 , Z 2 , ..., Z n ) =
n
∏
i =1
( Z i | P a ( Z i )) , (3.9)
where
P a
(
Z i
) is the paren t no de of v ariable
Z i
. This is a general form of equation
(3.1)
.
With the join t probabilit y and Ba yes theorem w e can infer p osterior probabilit y . More
BN structures and inference metho ds can b e found in Neap olitan et al. (2004) and
Murph y and R ussell (2002).
16

3.1 Mac hine learning mo del
X
Q
𝑃 𝑋 = 𝑥 𝑄 = 𝑖

𝑥 = [𝑥 1 , 𝑥 2 ] 𝑇

𝑖 = {1, 2}

Q

P
1 0.5
2 0.5
Prior
Conditional probability
Figure 3.2: The Ba y esian net w ork for data classification problem.
3.1.3 Gaussian mixture mo del
Let us tak e an example of data classification problem. Figure 3.2 presen ts a BN with
one discrete no de
Q
and one con tin uous no de
X
. In a BN with the mixture of b oth
discrete and con tin uous no des, discrete no des are usually represen ted as squares and
con tin uous no des as circles. So, no de
Q
is a discrete v ariable with binary states and
X
is a con tin uous v ariable that ob eys certain distribution. W e set the prior of
Q
as 0.5
for b oth t w o random v ariable v alues. This means that the c hance of
Q
= 1 and
Q
= 2
is equal. W e do not kno w what distribution
X
ob eys, but w e can observ e some v alues
of random v ariable
X
giv en
Q
= 1 and
Q
= 2 , see Figure 3.3a. The red p oin ts are
generated when
Q
= 1 and the blue are from
Q
= 2 . The problem is that giv en some
new data ho w can w e kno w in whic h class they are from? In Figure 3.3b, w e observ e
new data p oin ts, are they red or blue?
-4 -2 0 2 4
(a)
-2
-1
0
1
2
x2
Two datasets
Q = 1
Q = 2
-4 -2 0 2 4
(b)
-2
-1
0
1
2
x2
Given new data
Q = 1 or 2 ?
x1 x1
Figure 3.3: An example of data classification problem.
17

3. Mathematical bac kground
Without loss of generalit y , w e assume the blac k p oin ts are generated b y
Q
= 1 . So
w e can kno w ho w m uc h this assumption can b e trusted b y calculating the p osterior
probabilit y P ( Q = 1 | X ) using Ba y es theorem 3 :
P ( Q = 1 | X ) = P ( X | Q = 1) · P ( Q = 1)
P ( X ) . (3.10)
Because the mar ginal distribution
P
(
x
) is v ery difficult to get,
P
(
Q
= 1
| X
) can
normalized in the form of:
P ( Q = 1 | X ) = c · P ( X | Q = 1) · P ( Q = 1) , (3.11)
where c is a constan t v alue to mak e sure the p osterior probabilit y sum to one.
Then the problem of classification b ecomes ho w to fit the conditional probabilit y
densit y
p
(
x | Q
= 1) . Figure 3.4a sho ws the observ ed
x
v alues giv en
Q
= 1 . Since
Gaussian distribution is the most imp ortan t and most widely used distribution in
statistics, w e assume p ( x | Q = 1) is sub ject to Gaussian distribution:
p ( x | Q = 1) = N ( µ , Σ 2 ) , (3.12)
where µ , Σ are the mean and co v ariance of the observ ed data in Figure 3.4a.
The Gaussian fitting result can b e seen in Figure 3.4b. Ho w ev er, from the probabilit y
con tour of the fitted distribution w e ma y disb eliev e our assumption that the conditional
distribution is sub ject to Gaussian distribution. A ctually in Figure 3.4c w e can see that
the Gaussian con tour whic h represen ts data from Q = 1 largely o v erlaps the blue data
whic h generated from
Q
= 2 . This result mak es us reject the Gaussian assumption. But
it do es not mean that w e cannot use Gaussian metho d to fit
p
(
x | Q
= 1) . Gaussian
mixture mo del (GMM) is an alternativ e solution.
3
F or any giv en ev en t A and B, the probability of A giv en B can b e written as :
P
(
A | B
) =
P ( B | A ) · P ( A )
P ( B )
,
where P ( B )  = 0 .
18

3.1 Mac hine learning mo del
-4 -2 0 2 4
(a)
-2
-1
0
1
2
x2
Observed data
-4 -2 0 2 4
(b)
-2
-1
0
1
2
x2
Gaussian fitting
-4 -2 0 2 4
(c)
-2
-1
0
1
2
x2
Q = 1
Q = 2
p(x|Q=1)
x1
x1
x1

Figure 3.4: Fitting p ( x | Q = 1) with Gaussian distribution.
GMM is a parametric probabilit y densit y function represen ted as a w eigh ted sum
of Gaussian comp onen t densities (Reynolds, 2015). If random v ariable
p
(
x | Q
= 1) is
sub ject to certain GMM, it can b e written as:
p ( x | Q = 1) =
m
∑
i =1
w i N ( µ i , Σ i ) (3.13)
where
m
is the n um b er of mixture comp onen ts,
w i
is the w eigh t of
i th
Gaussian
distribution with 0
≤ w i ≤
1
, ∑ m
i =1 w i
= 1 , and
µ i , Σ i
are the mean and co v ariance of
i th Gaussian distribution, resp ectiv ely .
Figure 3.5 sho ws the fitting results b y GMM with 2 and 3 mixture comp onen ts,
resp ectiv ely . W e can see that with 2 mixture comp onen ts (Figure 3.5a), the probabilit y
con tour is m uc h b etter than it is in Figure 3.4b. And with 3 mixture comp onen ts, the
fitting result lo oks ev en b etter in Figure 3.5b.
19

3. Mathematical bac kground
-4 -2 0 2 4
(a)
-2
-1
0
1
2
x2
GMM fitting with m = 2
Q = 1
p(x|Q=1)
-4 -2 0 2 4
(b)
-2
-1
0
1
2
x2
GMM fitting with m = 3
Q = 1
p(x|Q=1)
x1
x1

Figure 3.5:
Fitting
p
(
x | Q
= 1) with GMM b y differen t n um b ers of mixture comp onen ts.
F rom the probabilit y con tour in Figure 3.6, it can b e found that using GMM with 3
mixture comp onen ts to fit the conditional probabilit y densit y
p
(
x | Q
= 1) , data from
Q
= 1 and
Q
= 2 can b e classified in an efficien t w a y . This is ho w GMM w orks to fit
probabilit y distribution.
-4 -2 0 2 4
x1
-2
-1
0
1
2
x2
Q = 1
Q = 2
p(x|Q=1)

Figure 3.6: The classification result of using GMM with 3 mixture comp onen ts.
3.1.4 Other ML mo dels
Although BN and GMM is the main ML mo dels detailed in this dissertation, ho w ev er
other ML mo dels e.g. supp ort v ector mac hine (SVM), naiv e Ba y es (NB), decision tree
(DT) and k-nearest neigh b ors (KNN), are also imp ortan t mo dels in the field of mac hine
learning.
Supp ort v ector mac hine
The supp ort v ector mac hine (SVM), in dealing with classification problems, is a
sup ervised learning metho d that generates h yp erplane functions from a set of lab eled
training data. With the decision b oundary functions, new data can th us b e classified
in to differen t classes.
20

3.1 Mac hine learning mo del
Supp ose w e ha v e data come from t w o classes. An SVM classifies data b y finding
the b est h yp erplane that separates all data p oin ts of one class from another. The b est
h yp erplane for an SVM is with the largest margin b et w een the t w o classes. The margin
is the maximal width of the slab parallel to the separating h yp erplane that has no
in terior data p oin ts (W ang, 2005). The supp ort v ectors are the data p oin ts that are
closest to the h yp erplane, whic h are on the b oundary of the slab. Figure 3.7 illustrates
these definitions.
Class 1
Class 2
Support v ector s
Figure 3.7: An example of ho w SVM w orks to classify data from t wo classes.
The ab o v e example is a linear classification case, whic h means that the b est
h yp erplane can b e describ ed b y a linear function. F or the case whose h yp erplane
cannot b e presen ted b y a nonlinear function (in the space whose dimension is the same
as the input data) is the non-linear classification problem. F or non-linear classification
problems, k ernel function
K
(
x, x ′
) is used to transform the data from the curren t space
to a new space where the h yp erplane can b e describ ed as a linear function. This pro cess
can b e illustrated in Figure 3.8. Common k ernel functions are linear k ernel, p olynomial
k ernel and Gaussian k ernel. The c hosen of certain k ernel function in a SVM is dep ending
on the training data, and the guidance can b e found in Sc holk opf and Smola (2001) and
Ben-Hur and W eston (2010).
21

3. Mathematical bac kground
𝑘 (𝑥 , 𝑥 ′ )

Figure 3.8:
Using k ernel function to transform data from one space to another, where
the h yp erplane used for classification can b e describ ed b y a linear function.
The adv an tages of supp ort v ector mac hine is that it p erforms w ell and memory
effectiv e ev en with high dimensional features, ho w ev er, when the n um b er of feature
dimension is m uc h greater than the n um b er of training samples, o v er-fitting could
happ en (Smola and Sc hölk opf, 2004).
Naiv e Ba y es
The naiv e Ba y es (NB) classifier is a sup ervised learning algorithm based on Ba y es theorem
with the simple assumption of conditional indep endence b et w een eac h feature set giv en
differen t classes. Assume a set of training data with feature set
x
= [
x 1 , ..., x t ..., x n
]
T
(
x i
means the i-th feature) are lab eled b y class
C
, so based on Ba y es theorem the predicted
probabilit y giv en features can b e written as:
P ( C | x 1 , x 2 , ..., x n ) = P ( C ) · P ( x 1 , x 2 , ..., x n | C )
P ( x 1 , x 2 , ..., x n ) , (3.14)
and with the conditional indep enden t assumption:
P ( x 1 , x 2 , ..., x n | C ) =
n
∏
i =1
P ( x i | C ) , (3.15)
th us equation (3.14) is simplified to:
P ( C | x 1 , x 2 , ..., x n ) = P ( C ) · ∏ n
i =1 P ( x i | C )
P ( x 1 , x 2 , ..., x n ) . (3.16)
Since P ( x 1 , x 2 , ..., x n ) is a constan t, equation (3.16) is then written as:
P ( C | x 1 , x 2 , ..., x n ) ∝ P ( C ) ·
n
∏
i =1
P ( x i | C ) , (3.17)
where
P
(
x i | C
) is the conditional distribution needs to b e fitted. The common
distributions used to fit the conditional distribution are Gaussian distribution, k ernel
distribution as w ell as m ultiv ariate m ultinomial distribution etc., whic h are detailed
in Manning, Ragha v an, and Sc h ütze (2010).
22

3.1 Mac hine learning mo del
Naiv e Ba y es classifiers can b e extremely fast compared to more sophisticated metho ds.
The decoupling of the class conditional feature distributions means that eac h distribution
can b e indep enden tly estimated as a one dimensional distribution. This in turn helps to
alleviate problems stemming from the curse of dimensionalit y (Zhang, 2004). Ho w ev er,
the do wnside is also caused b y the conditional indep enden t assumption b et w een features
since some features are dep enden t and th us lead to p o or fitting problem.
K-nearest neigh b or
The nearest neigh b or metho d for classification is a t yp e of instance-based learning, whic h
means that it do es not attempt to construct a general in ternal mo del, lik e the men tioned
SVM and NB, but simply stores instances of the training data. Giv en new data, the
decision of classifying is computed from a simple ma jorit y v ote of the nearest neigh b ors
of eac h new data p oin ts: a query p oin t is assigned the data class whic h has the most
represen tativ es within the nearest neigh b ors of the p oint (P edregosa et al., 2011).
In KNN, K is the n um b er of nearest neigh b ors. The n um b er of neigh b ors is the core
deciding factor. K is generally an o dd n um b er if the n um b er of classes is 2. When K=1,
then the algorithm is kno wn as the nearest neigh b or algorithm. Let us set an example.
Giv en a new p oin t (green triangle in Figure 3.9), for whic h a lab el needs to b e predicted,
KNN w orks in the follo wing pro cedures:
1.
Calculate distance: there are v arious metrics to determine the distance b et w een the
new p oin t and its neigh b ors, e.g. Euclidean distance, cit y blo c k metric, Mahalanobis
distance, Mink o wski metric, Cheb yc hev distance, Cosine distance, Jaccard distance,
Correlation distance etc.. One can c ho ose them dep end on certain requiremen t.
2.
Find closest neigh b ors: select the closest neigh b ors dep ends on K. If K=1, then only
one neigh b or is selected (Figure 3.9a) and for K=3, three neigh b ors are selected
(Figure 3.9b).
3.
V ote for lab els: classify p oin ts b y the ma jorit y v otes of its k neigh b ors. Eac h ob ject
v otes for their class and the class with the most v otes is tak en as the prediction.
In Figure 3.9b three neigh b ors are selected, with t w o p oin ts v oting for class 1 and
one for class 2, so the new p oin t is predicted as class 1.
F rom the ab o v e example w e can see that K do es matter for new p oin t prediction,
ho w ev er, there are no optimal n um b er of neigh b ors that suits all kind of data sets. Eac h
dataset has its o wn prop ert y . Generally , K can also b e c hosen b y generating the mo del
on differen t v alues of K and c hec k their p erformance. The detailed guidance of c ho osing
K can b e found in (F riedman, 1997).
23

3. Mathematical bac kground
K = 1
?
?

?

?

?

?

?

?

?

Class 1
Class 2
New dat a
(a) KNN finding closest neigh b ors for the case K=1.
Class 1
Class 2
New dat a
?
K = 3
?

?

?

?

?

?

?

?

(b) KNN finding closest neigh b ors for the case K=3.
Figure 3.9: An example of ho w KNN w orks for classification of new data.
Decision tree
Decision T ree (DT) is a non-parametric sup ervised learning metho d, whic h creates a
mo del that predicts the new data for whic h class it comes from b y learning simple
decision rules inferred from the features. Here is an example to show ho w DT w orks for
making decision of Whether to play tennis?
4
. The decision of Whether to play tennis?
is influenced b y sev eral conditions, e.g. outlo ok of the w eather, temp erature, h umidit y ,
wind etc. The decision tree of Whether to play tennis? can b e dra wn in Figure 3.10.
4
This example is from the presen tation in a mac hine learning course made by Prof. Dr.
Martin Riedmiller of Alb ert-Ludwigs-Univ ersität F reiburg, full source link: h ttp://ml.informatik.uni-
freiburg.de/former/_media/teac hing/ss10/03_decisiontrees.pdf, visited on 31.07.2019
24

3.1 Mac hine learning mo del
W eather
Rain
Wind
Sunny
No
Strong
W eak
Humidity
Y es
Normal
No
High
No
T empera
-ture
Y es
Hot Normal

Figure 3.10: The decision tree of Whether to play tennis? .
A decision tree is dra wn upside down with its ro ot at the top, whic h is W e ather in
this case. The b old text in ellipse represen ts a condition no de, and the blue texts are
the status of the condition. Based on the condition no de, the tree splits in to branc hes.
The end of the branc h that cannot split an ymore is the leaf, in this case, whether to pla y
tennis, represen ted as red and green text resp ectiv ely . In the real case of classification,
a decision tree is trained b y more features and has more branc hes with more complex
structure. In practice, it is tric ky to set when the branc h to split and when stop splitting.
Because if the dataset has a large n um b er of features, it results in large n um b er of splits,
whic h in turn giv es a h uge tree. Suc h trees are complex and can lead to o v er-fitting
problem. F or more guidance of configuring DT s see (Safa vian and Landgreb e, 1991).
The decision tree mo del for classification is easy to understand and only requires a
small amoun t of training dataset. In addition, little data preparation is needed to train
a decision tree, i.e. data normalization and dumm y v ariables are not necessary b ecause
decision tree can use the original feature to split branc hes. Ho w ev er, the decision tree
can b e also sensitiv e to the data, b ecause small v ariations in the data migh t result in a
completely differen t tree b eing trained.
25

3. Mathematical bac kground
3.2 P arameter learning
In this thesis, the exp ectation-maximization (EM) algorithm is used to learn the
parameters of a GMM (Bishop, 2006). The EM is an iterativ e algorithm that initiates
from a random
θ
and then pro ceeds to iterativ ely calculate the log-lik eliho o d of
θ
and
up date till the lik eliho o d b ecomes con v ergence. Eac h iteration consists an E-step and
M-step (Bishop, 2006):
• Initialize θ i = [ w i , µ i , Σ i ] and ev aluate the initial v alue of the log-lik eliho o d:
l ( θ ) = l og ( p ( x | θ )) =
N
∑
j =1
l og { m
∑
i =1
w i N ( µ i , Σ i ) } (3.18)
where x = [ x 1 , ..., x N ] .
•
E-step: here a laten t v ariable
γ i
is in tro duced to represen t the p osterior probabilities
of P ( θ | x ) , and based on Ba y es rule, it could b e written as
γ i ( x ) = w i N ( µ i , Σ i )
∑ m
j =1 w j N ( µ j , Σ j ) (3.19)
•
M-step: Re-estimate the parameters using curren t p osterior probabilities, so the
new θ can b e written as
µ N ew
i = ∑ N
j =1 γ i ( x j ) x j
∑ N
j =1 γ i ( x j )
Σ N ew
i = ∑ N
j =1 γ i ( x j )( x j − µ i )( x j − µ i ) T
∑ N
j =1 γ i ( x j )
w N ew
i = 1
N
N
∑
j =1
γ i ( x j )
(3.20)
After estimation of new θ N ew up date the log-lik eliho o d:
l ( θ N ew ) =
N
∑
j =1
l og { m
∑
i =1
w N ew
i N ( µ N ew
i , Σ N ew
i ) } (3.21)
The iteration equation
(3.19)
-
(3.21)
are rep eated un til
l
(
θ N ew
)
− l
(
θ
)
< ε
, where
ε
is a
v ery small p ositiv e v alue.
26

3.3 Ev aluation metho d
3.3 Ev aluation metho d
3.3.1 Receiv er op erating c haracteristic
One of the most p opular metho ds of ev aluating classification or prediction p erformance
are receiv er op erating c haracteristic (R OC) curv es. Regarding to LC b eha vior researc h
using R OC curv e metho d can b e found in McCall et al. (2007), Liebner et al. (2013),
P eng et al. (2015), Doshi and T rivedi (2009), and Lethaus, Baumann, et al. (2013).
The R OC curv e is created b y plotting the true p ositiv e rate (TPR) against the false
p ositiv e rate (FPR) at v arious threshold settings. TPR and FPR are computed b y
TPR = T P
T P + F N
FPR = F P
T N + F P
(3.22)
where TP , TN, FP and FN are true p ositiv es, true negativ es, false p ositiv es, and false
negativ es, resp ectiv ely . Th us TPR and FPR are the function of threshold T, termed as
TPR = ∫ ∞
T f 1 ( x ) dx
FPR = ∫ ∞
T f 2 ( x ) dx
(3.23)
3.3.2 Area under curv e
The metric of using R OC curv e to compare mo del p erformance o v er differen t metho ds
is to calculate the v alue of area under curv e (A UC) of eac h R OC curv e. A UC is giv en
b y F a w cett (2006):
AU C = ∫ −∞
∞
TPR ( T ) FPR ′ ( T ) dT , (3.24)
where T is the differen t threshold setting. Basically , A UC is ranging from 0 to 1 and a
larger A UC v alue indicates b etter p erformance.
3.3.3 Cross-v alidation
T raining a prediction mo del and testing it on the same data is a metho dological mistak e.
This w ould lead to o v er-fitting, whic h means the mo del just rep eats the lab els of the
samples that it has just trained with and could get a p erfect score with suc h trained data,
ho w ev er, w ould fail to predict an ything useful on non-trained data. T o a v oid o v er-fitting,
it is common practice when p erforming a sup ervised mac hine learning exp erimen t to
hold out part of the a v ailable data and train the mo del with the rest. In other w ords,
27

3. Mathematical bac kground
training data and test data should b e separated. This metho d is called cross-v alidation
(CV).
One p opular CV metho d is k-fold cross-v alidation. The original sample is randomly
partitioned in to
k
equal sized subsets. Of the
k
subsets, a single subset is retained as
the v alidation data for testing the mo del, and the remaining
k −
1 subsets are used as
training data. The cross-v alidation pro cess is then rep eated
k
times, with eac h of the
k
subset used exactly once as the v alidation data. The
k
results can then b e a v eraged to
pro duce a single estimation (McLac hlan, Do, and Am broise, 2005).
3.4 Summary
In this c hapter, the core mac hine learning mo de, i.e. Ba y esian net w ork and Gaussian
mixture mo del, as w ell as the dominated parameter learning algorithm, i.e. the
exp ectation–maximization algorithm are detailed. A t the same time, supp ort v ector
mac hine, Naive Ba y es, K-nearest neigh b or as well as decision tree are also explained.
In order to explain the math b ehind eac h mo del in an easy-to-understand w a y , this
dissertation uses some liv ely examples to unco v er the m ystery of the theories. In addition,
ev aluation metho ds that are used for ev aluating classification result are also in tro duced
here. All these theories are the bac kb one of this dissertation.
28

4
Exp eriment 1 - Prediction of driver
lane-change b ehavio r based on a
driving simulato r exp eriment
©
2018 IEEE. Reprin ted, with p ermission, from Xiaohan Li (the author of this
dissertation), W ensh uo W ang, and Matthias Ro etting, Estimating Driv er’s Lane-Change
In ten t Considering Driving St yle and Con textual T raffic, IEEE T ransactions on In telligen t
T ransp ortation Systems, Octob er 2018. DOI: 10.1109/TITS.2018.2873595.
A substan tial p ortion of this c hapter is based on the ab o ve paper.
4.1 In tro duction
In c hapter 2, the related w orks regarding to prediction of driv er LC b eha vior ha v e b een
in tro duced, ho w ev er, to design a comprehensiv e framew ork is no easy task. There are
still some asp ects that should b e tak en in to accoun t or can b e impro v ed.
Con textual traffic
In c hapter 2, w e men tioned t w o t yp es of lane-c hange, i.e. mandatory lane-c hange (MLC)
and discretionary lane-c hange (DLC). Since the purp oses of MLC and DLC are differen t,
the decision-making pro cess is differen t as w ell. Researc h found that most driv ers who
w ere in v olv ed in LC crashes did not attempt an a v oidance maneuv er (Olsen, Lee, and
Wierwille, 2005). This suggests that the driv er did not see or w as una w are of the
presence of another v ehicle or crash hazard (Tijerina, 1999). In this situation, the driv er
fails to understand the danger of the curren t driving con text.
A ctually , con textual traffic is closely related to driv er b eha vior. F or instance, it
impacts driv er gaze b eha vior. Lee, Olsen, Wierwille, et al. (2004) found that driv er
29

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
glance duration can increase b y 0.25 s on a v erage (a 20% increase) in driving situation
where an o v ertaking v ehicle app eared, in comparison to the situation with no traffic
in v olv ed. More sp ecifically , single glance duration w as ranging from 1.1 s to 1.8 s (Mean
= 1.25 s) when no o v ertaking o ccurred in the adjacen t lane, and from 1.0 s to 2.3 s
(Mean = 1.5 s) when o v ertaking o ccurred in the adjacen t lane. Ov erall, the road traffic
caused a large (50% to 85%) increase in b oth total and visual input times. Without the
road traffic, visual searc h times w ere 3.7 s for left LC and 3.4 s for righ t LC. If there
w ere more road traffic, visual searc h times w ere 6.1 s for left LC and 4.5 s for right LC.
Considering that driv er gaze b eha vior is an imp ortan t predictor of driv er LC b eha vior,
the effect of c ontextual tr affic should b e considered. Beggiato et al. (2018) found that
b y monitoring driv er gaze b eha vior the n umber of glances in a sequence w as primarily
asso ciated with the road traffic densit y on the target lane. There are more v ehicles on
the target lane, the more and longer the driv er glancing mirror b eha vior can b e observ ed.
Therefore, to predict driv er LC b eha vior, the curren t con textual traffic should b e tak en
in to accoun t.
Driving st yle
As long as the driv er remains a part of the con trol lo op, driving and safet y b eha viors
are more than just the mec hanical op eration of a v ehicle (Hennessy, 2011), but also
affected b y the driving st yle of the driv er. The concept of driving st yle can b e termed
as either a dynamic b eha vior of a driv er on the road (Murphey, Milton, and Kiliaris,
2009) or an in trinsic driving habit (Saad, 2004; Sagb erg et al., 2015).
The former concept tends to consider driving st yle as a transien t b eha vior, whic h
means that a driv er can b e aggressiv e at one time p erio d but normal at other situation.
Murphey, Milton, and Kiliaris (2009) classified the dynamic driving st yle as
calm driving
,
aggressiv e driving
and
normal driving
. V elo cit y , acceleration and jerk w ere used as the
measuremen ts. The result suggested that driv ers with aggressiv e driving ha ve more fuel
consumption.
The latter concept of driving st yle, how ev er, is more p opular since differen t driving
habits ma y affect the design, effectiv eness, and feedbac k mec hanisms of future AD AS. F or
example, Doshi and T riv edi (2010) found that aggressiv e driv ers are more consisten t in
b eha viors and more predictable than non-aggressiv e driv ers. In a pap er b y Johnson and
T riv edi (2011), it w as found that aggressiv e and non-aggressive driv ers tend to b eha v e
in differen t w a ys in the similar situation. Non-aggressiv e driv ers are quan tifiable and
significan tly more complian t to feedbac k from AD AS. It indicated that the p opulations
of non-aggressiv e driv ers need to b e further split in order to detect more significan t
b eha vioral trends. By monitoring driv er gaze b eha vior, the results sho ws that when
the driv er is conducting a LC to o v ertake cars, conserv ativ e driv ers prefer a higher
time-headw a y (Mean = 1.76 s) than either the neutral driv ers (Mean = 1.23 s) or the
aggressiv e driv ers (Mean = 1.15 s). So, it is reasonable to sa y that driv ers with differen t
30

4.1 In tro duction
driving st yles w ould ha v e differen t preference when initiating a LC in an o v ertaking
scenario (F airclough, Ma y, and Carter, 1997).
In conclusion, the driving st yle should b e considered in prediction of driv er LC
b eha vior.
Data lab eling metho d
The related w orks regarding to the prediction of driv er b eha vior mainly use sup ervised
mac hine learning mo dels (P en tland and Liu, 1999; Kasp er et al., 2012; Liebner et al.,
2013; Jain, K oppula, Ragha v an, et al., 2015; P eng et al., 2015). The fact is that to train
go o d sup ervised learning mo dels are strongly dep ended on high-qualit y lab eled data.
T o some degree, mo del p erformance is closely related to the qualit y of the lab eled data.
Sp ecifically for prediction of LC, ho w to lab el LC and lane-k eep (LK) data samples from
the ra w dataset is a big issue b ecause differen t training datasets could train differen t
parameters.
Most of the related w orks applied a time-windo w (TWL) to lab el LC datasets, termed
as the TWL metho d. A time-windo w with a fixed length of duration is used to lab el
time series data. This metho d is commonly used b y lab eling t w o adjacen t ev en ts in time
series. By using the TWL metho d, data samples, whic h are within certain time-windo w
b efore the momen t that one sp ecific part of the v ehicle just hits the lane b oundary , are
lab eled as LC datasets (Mandalia and Salvucci, 2005; Doshi and T riv edi, 2009; Lethaus,
Baumann, et al., 2013; Doshi and T riv edi, 2008; Morris, Doshi, and T riv edi, 2011).
The limitation of the TWL metho d is that the fixed TW that is used to distinguish
LC and LK ev en ts is the same for all the driv ers, regardless of differen t driving situations.
Ho w ev er, in practice the driv er could start to prepare for a LC either early or late
dep ends on traffic situation and his/her p ersonal preference. In other w ords the suitable
TW dep ends on the con textual traffic as w ell as driving st yle. F or instance, what ma y
happ en in a complex situation is that the driv er attempts to mak e LC and afterw ards
he/she ab orts his/her in ten tion. In other w ords, driv er LC b eha vior is v ery driv er and
situation sp ecific. Rehder et al. (2016) made a statistical analysis whic h coun ted ho w
early the driv er tends to start a LC b eha vior until the fron t wheel of the car just hits
the lane b oundary . The duration of this pro cess suffers a high v ariance of 2.42 s, and
this large difference do es impair the prediction p erformance based on the results.
Therefore, a data lab eling metho d that considers LC b eha vior case b y case could
impro v e the qualit y of the lab eled datasets.
Motiv ation of this study
In this c hapter w e aim to design a framew ork whic h can b e used for prediction of driv er
LC b eha vior. The exp erimen t w as conducted in a seat-b o x based driving sim ulator. F or
the purp ose of making the sim ulated driving en vironmen t easier but without loss of
31

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
generalit y , the driving scenario w as mo deled on a t w o-lane high w a y and for the case of
left lane-c hange (LLC). Inspired b y the related w orks, sev eral asp ects that are discussed
ab o v e could b e impro v ed through this study:
1.
Considering con textual traffic, a cell-grid metho d is implemen ted to mo del the
curren t driving situation.
2.
Considering driving st yle, all the participan ts are classified in to three groups, i.e.
high aggressiv e, medium aggressiv e and lo w aggressiv e driving st yle b y using a
b eha vioral-psyc hological questionnaire.
3.
In order to obtain high-qualit y lab eled datasets for mo del training, a gaze-based
lab eling (GBL) metho d is implemen ted. With the GBL metho d, LC datasets can
b e lab eled based on the momen t that the driv er really tends to mak e LC rather b y
assuming a fixed TW.
4.
In the pro cess of preparing for training datasets, the lab eled datasets are man ually
organized b y differen t driving scenarios as w ell as b y differen t driving st yles.
4.2 Driving scenario
4.2.1 Mo deling con textual traffic
In order to mo del con textual traffic, the road traffic should b e sp ecified. T w o kinds
of v ehicles, i.e. sub ject v ehicle and nearest surrounding v ehicles, are considered in the
sp ecified scenarios (Figure 4.1). The sub ject v ehicle (blue) is the host vehicle dro v e b y
the participan t. V ehicle 1, V ehicle 2, and V ehicle 3 are the nearest surrounding v ehicles
(red) to the sub ject v ehicle on the curren t lane and/or on the target lane. The target
lane is to whic h the sub ject v ehicle wan ts to c hange. In order to term the scenarios
easily , w e define the LC scenarios as follo ws in Figure 4.1:
•
Sc enario le ad only (Figure 4.1a): There is no v ehicle on the target lane within a
sp ecific range. The only surrounding v ehicle is V ehicle 1 in fron t of the sub ject
v ehicle.
•
Sc enario le ad + adjac ent b ehind (Figure 4.1b): The sub ject v ehicle in tends to mak e
LLC to o v ertak e the slo w leading v ehicle, mean while, a fast mo ving v ehicle (V ehicle
2) is approac hing from left b ehind on the target lane.
•
Sc enario le ad +2 adjac ent (Figure 4.1c): The sub ject v ehicle in tends to mak e LLC
to o v ertak e the slo w leading v ehicle, b y then a left fron t v ehicle (V ehicle 3) and a
left b ehind v ehicle (V ehicle 2) are on the target lane.
There are some existing w orks regarding to driving con textual traffic mo deling.
A p oten tial field diagram comp osed of bubbles with differen t dynamic sizes has
b een prop osed to describ e the dynamic relationship b et w een the sub ject v ehicle and
32

4.2 Driving scenario
V ehicle 1
Subject vehicl e
T arg et lane
Curren t lane

(a) A slo w leading v ehicle in front of the subject vehicle on the curren t lane.
V ehicle 1
Subject vehicl e
V ehicle 2

(b)
A v ehicle on the target lane with a slo w leading vehicle in fron t of the
curren t lane.
V ehicle 1
V ehicle 2 V ehicle 3
Subject vehicl e

(c)
T wo v ehicles on the target lane with a slo w leading v ehicle in front of the
curren t lane.
Figure 4.1:
Illustration of the defined LLC scenarios, (a) Scenario lead only (b) Scenario
lead + adjacen t b ehind and (c) Scenario lead +2 adjacen t.
33

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
surrounding v ehicles (W o o et al., 2016). Ho w ev er, it is not a direct mo deling metho d
whic h can b e used for driving con textual traffic analysis. Leonhardt and W anielik (2017)
dev elop ed a probabilistic situation assessmen t mo del to ev aluate the safet y state of
the sub ject v ehicle with surrounding v ehicles, ho w ev er, it aims at recognizing driv er
b eha vior rather than mo deling driving scenarios.
T o easily describ e the relationship b et w een the sub ject v ehicle and the surroundings,
one of the most p opular approac hes is to segmen t the surrounding traffic in to grid
cells (Do et al., 2017; Nilsson, Silvlin, et al., 2016; Kasp er et al., 2012) and then
mo del the dynamic relationship of these grid cells. The o ccupancy status of eac h cell
is represen ted b y a binary v alue, i.e. o ccupied or empt y . Since the exp erimen t in this
study is only fo cused on the t w o-lane high w a y , a fiv e-cell con textual grid is enough to
describ e the con textual traffic whic h is demonstrated in Figure 4.2:
• f : the cell in fron t of the sub ject v ehicle on the curren t lane.
• b : the cell b ehind the sub ject v ehicle on the curren t lane.
• l : the cell on the left of the sub ject v ehicle on the target lane.
• f l : the cell in fron t of the sub ject v ehicle on the target lane.
• bl : the cell b ehind the sub ject v ehicle on the target lane.
where
f , b, l , f l , bl ∈ { 0 , 1 }
are the status of eac h cell. The status in the corresp onding
cell is 1 if the cell is o ccupied b y v ehicles otherwise it is 0 for uno ccupied. The length of
cell
l
is the length of the sub ject v ehicle plus a short safet y distance. Here, the length
of cell
l
is set as 5 m. A ccording to A yres et al. (2001), the preferred car-follo wing
time-headw a y ranges from 1 s to 2 s on high w a ys. Based on the traffic densit y study in
F airclough, Ma y, and Carter (1997), the a v erage time-headwa y for conserv ativ e driv ers
to initiate o v ertaking maneuv ers is 1.76 s. Time-headw a y is often determined b y traffic
coun ting devices that determine the in ter-arriv al times of v ehicles. In our case, the
time-gap (net time-headw a y) (Ha, Aron, and Cohen, 2012) is used to measure the
in ter-v ehicle distance since it is usually measured via radars or LiD AR moun ted on the
sub ject v ehicle and presumably reflected b y the rear of the leading v ehicle (J2944, 2013).
The length of the fron t and rear cell grids is set to a time-gap of 2 s, see Figure 4.2.
fl
l
bl
f
b
5m
2s time g ap 2s time g ap

Figure 4.2: Illustration of the o ccupancy grid on a t w o-lane high w ay .
34

4.2 Driving scenario
4.2.2 F eature extraction
Selecting suitable features is b eneficial to impro v e mo del p erformance. Both
TTC (Ha yw ard, 1972) and time-gap are the preferable features to analyze or mo del LC
b eha vior (Zhao, Lam, et al., 2017; W akasugi, 2005). TTC is the time required for t w o
v ehicles to collide if they con tin ue at their present speed and on the same path. It is
usually used to measure collision risk (Kusano and Gabler, 2011). Time-gap is often
used to assess safe car-follo wing distance (T ordeux, Lassarre, and Roussignol, 2010). W e
select time-gap and TTC as prediction features whic h are defined according to J2944
(2013). The parameters of the definition can b e seen in Figure 4.3:
• TTC with resp ect to the v ehicle in cell f :
ttc f = d 1
v 0 − v 1
, (4.1)
where
d 1
is the distance b et w een the fron t edge of the sub ject v ehicle and the rear
edge of V ehicle 1,
v 0
and
v 1
are the sp eed of the sub ject v ehicle and V ehicle 1,
resp ectiv ely .
• Time-gap with resp ect to the v ehicle in cell f :
tg ap f = d 1
v 0
, (4.2)
• TTC with resp ect to the v ehicle in cell bl :
ttc bl = d 2
v 2 − v 0
, (4.3)
where
d 2
is the distance b et w een the front edge of v ehicle 2 and the rear edge of
the sub ject v ehicle. v 2 is the sp eed of v ehicle 2.
• TTC with resp ect to the v ehicle in cell f l :
ttc f l = d 3
v 0 − v 3
, (4.4)
where
d 3
is the distance b et w een the fron t edge of the sub ject v ehicle and the rear
edge of V ehicle 3.
v 0
and
v 3
are the sp eed of the sub ject v ehicle and V ehicle 3,
resp ectiv ely .
When the sp eed of the sub ject v ehicle is close to the sp eed of V ehicle 1, then equation
(4.1)
approac hes to infinit y . T o a v oid this case, w e use the in v erse of
ttc f
, noted as
ttc − 1
f
, as
the feature. Similarly , w e compute the in v erse of
ttc f l
,
ttc bl
,
tg ap f
and then obtain
ttc − 1
f l
,
ttc − 1
bl , tg ap − 1
f , resp ectiv ely .
35

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
V ehicle 1
V ehicle 2 V ehicle 3
𝑑 2
𝑑 3
𝑑 1
𝑣 0
𝑣 2
𝑣 1

Figure 4.3: Illustration of the parameters regarding to the features.
4.3 Exp erimen t
4.3.1 Exp erimen tal setup
Driving sim ulator
The exp erimen ts w ere conducted in a seat-b o x based driving sim ulator. The steering
wheel con trol system w as a F anatec ClubSp ort Wheel Base V2.5 with torque feedbac k.
The soft w are w e used to create the driving scenario is Op enDS Pro 3.5
5
. There are
sev eral basic driving scenarios suc h as urban roads, high w a ys as w ell as coun tryside. In
order to design the driving task, the basic driving scenarios w ere extended to a t w o-lane
motorw a y scenario used for our driving exp erimen t.
F or the surrounding v ehicles in the scenarios, w e set the sp eed on four lev els, i.e. 100
km/h, 120 km/h, 140 km/h, and 150 km/h. The length of eac h car is 4 m. V ehicles on
the left lane w ere mo ving faster than v ehicles on the righ t lane. Figure 4.4 depicts that
one participan t is doing the driving sim ulator exp eriments.
Ey e-trac k er
The ey e-trac k er used to monitor driv er gaze b eha vior is a SMI ETG (SMI, 2015), see
Figure 4.5. SMI ETG can record video from the first-p erson view. W e pro cess the
ey e-trac king data through BeGaze 3.6, whic h is an official soft w are from SMI. In order
to map driv er gaze b eha vior, w e define 5 areas of in terests (A oI) in Figure 4.6, i.e. R e ar
mirr or , L eft mirr or , R ight mirr or , Sp e e dometer and b esides Wind scr e en whic h is all the
reset area of the screen. Th us, A oIs of the participan t during the exp erimen t can b e
extracted frame b y frame b y BeGaze 3.6.
In order to sync hronize data collection from the driving sim ulator and the ey e-trac k er,
the sampling rate is set to 30 Hz for b oth devices.
5 h ttps://op ends.dfki.de/ (visited on 04.2016)
36

4.3 Exp erimen t
Figure 4.4: A participan t is doing exp eriment on the sim ulator.
Figure 4.5:
The ey e-tracking used in the experiment. Picture extracted from the
link: https://www.smivision.com/ey e-trac king/pro ducts/mobile-ey e-trac king/ (visited on
31.07.2019).
37

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
Rear mirror
Left mirror
Speedometer
Right mirr or
Figure 4.6: The A oIs of the driv er in a frame using BeGaze 3.6.
Driving task
Before the exp erimen t, the participan ts w ere ask ed to fill in a demographic questionnaire.
The details can b e found in App endix A.1.1. After finishing the demographic
questionnaire, the participan ts w ere giv en an oral instruction, including the calibration of
the ey e-trac k er and a short description of the driving task etc. The detailed instruction
in text form can b e found in App endix A.1.3 and A.1.4.
After calibration, participan ts w ere guided to driv e in a w arming-up scenario to
b e familiar with the sim ulator. The driving tasks w ere designed based on the three
scenarios men tioned in Figure 4.1. P articipan ts w ere instructed to driv e on the righ t lane
throughout the exp erimen t and to only use the left lane for the purp ose of o v ertaking
v ehicles. Figure 4.8 giv es an example of car-follo wing case where the participan t is
driving on the righ t lane b ehind a v ehicle. And at the same time w e can see from
the left view mirror that a v ehicle on the left lane is approac hing from b ehind, so the
participan ts ha v e to decide if it is safe to o v ertak e the slo w v ehicle.
No sp eed limit signs w ere set on the high w a y scenario since it is quite common in
German y . P articipan ts w ere allo w ed to driv e at their o wn sp eed preference to either
follo w or o v ertak e v ehicles. All participan ts to ok the iden tical driving task whic h lasts
ab out 40 min utes.
4.3.2 P articipan ts
In total, 32 participan ts (18 females and 14 males) with ages from 21 to 51 y ears (Mean
= 30.4 y ears) participated in the exp erimen t. All the participan ts had normal vision
and had held their driving licenses for a minim um of 1.5 y ears and maxim um of 27 y ears
(Mean = 10.7 y ears). Among all participan ts, 28 of them got paid for the exp erimen t
38

4.3 Exp erimen t
1 2
1-No experience, 2-Have experience
(a)
0
5
10
15
20 Experience of simulator test
80 100 120 140 160 180
Km/h
(b)
0
5
10
15
20 Driving speed preference
12345
1 = never; 5 = very often
(c)
0
5
10
15 Right lane-change overtaking frequency
12345
1 = very close; 5 = far away
(d)
0
5
10
15
20 Front distance when overtaking

Figure 4.7: Illustration of the driving habits of the participan ts in histogram.
Figure 4.8: A screen shot of driving sim ulator and driving scenarios.
39

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
and the others w ere either volun teers or studen ts who need credits. Figure 4.7 giv es
some driving bac kground information regarding to the participan ts. W e can see that
almost 2/3 of them ha v e exp erience of doing driving sim ulator exp erimen t and most of
them prefer to driv e o v er 120 km/h on high w a y . This sp eed preference is in accordance
with our sp eed setting. In addition, most of them rarely o v ertak e cars b y executing
righ t lane-c hange, since it is not allo w ed based on German traffic rules. More detailed
statistics of the participan ts ab out their bac kground can b e seen in App endix Figure A.1.
4.3.3 Driving st yle classification
In order to classify driving st yle, all the participan ts w ere ask ed to fill in a b eha vioral-
psyc hological questionnaire (in App endix A.1.2) used for ev aluating the aggressiv eness
of driving habit prop osed b y (Glaser and W asc h ulewski, 2005). A similar metho d
whic h uses questionnaire to ev aluate driving st yle can b e found in F renc h et al. (1993).
V öhringer-Kuhn t and T rexler-W alde (2005) listed the questions whic h can reflect driving
aggressiv eness. The correlation tests indicates that the driv er who ac hiev es a higher
score b y giv en sp ecific questions represen ts a higher aggressiv eness with Cron bac h’s
α
= 0
.
765 . Based on the their scores, participan ts w ere categorized in to three groups: 8
driv ers with scores b et w een 0 and 5 (Mean = 2.5) w ere categorized as the lo w aggressiv e
group, 15 driv ers with scores greater than 5 and smaller than 10 (Mean = 7) w ere
categorized as the medium aggressiv e group, and 9 driv ers with scores greater than
10 (M = 14.7) w ere categorized as the high aggressiv e group, resp ectiv ely . The whole
statistics ab out the aggressiv eness scores are illustrated in Figure 4.9.
-5 0 5 10 15 20 25 30
Score of aggressiveness
0
0.5
1
1.5
2
2.5
3
3.5
4

Figure 4.9: Illustration of the aggressiv eness scores of the participan ts in histogram.
40

4.4 Lane-c hange data lab eling
4.4 Lane-c hange data lab eling
Slow leading v ehicle
Subject vehicl e
𝑡 𝑝𝑟𝑒𝑝𝑎𝑟𝑒 𝑡 𝑐ℎ𝑎𝑛𝑔𝑒 𝑡 0
𝑡 𝑖𝑛𝑡𝑒𝑛𝑡

Figure 4.10: The k ey momen ts during a lane c hange course.
T o mak e the data lab eling pro cess easy to understand, w e define three k ey momen ts to
describ e driv er LC b eha vior as it is depicted in Figure 4.10. Firstly ,
t 0
is the momen t
that the fron t left wheel of the v ehicle just runs cross the cen tral dotted line, whic h
can b e mapp ed out b y lateral mo v emen t of the sub ject v ehicle. Secondly ,
t chang e
is the
momen t that the driv er really starts to execute an LC maneuv er, whic h can b e mark ed
b y finding the steering wheel angle threshold (Li, W ang, and Rötting, 2016). Ho w ev er,
it is difficult to find the momen t
t intent
. Previous studies found that driv er gaze b eha vior
giv es us clues to predict up coming b eha viors (Salvucci and Liu, 2002; Fitc h et al., 2009).
Result suggested that when LLC o ccurs, the driv er tak es 65% – 85% c hance to glance at
the left view mirror (Tijerina et al., 2005). Therefore, the glancing mirror b eha vior of
the driv er indicates a LC in ten tion. Inspired b y this result, a gaze-based lab eling (GBL)
metho d is prop osed. This GBL metho d lab els LC datasets based on driv er gaze b eha vior.
Since
t pr epar e
is differen t b y LC cases and b y differen t driv ers, the GBL metho d could
tak e adv an tage of driv er gaze b eha vior to capture this momen t exactly , compared with
the TWL metho d whic h uses a fixed time-windo w to assume
t intent
(Lethaus, Baumann,
et al., 2013; Doshi and T riv edi, 2009).
41

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
4.4.1 Gaze-based lab eling metho d
Lab eling criterion
The LC decision-making pro cedure is describ ed as follo ws: when the driv er w an ts to
mak e LC, he/she will firstly glance at the mirrors to chec k whether it is safe or not to
finish a successful LC maneuv er (Lee, Olsen, Wierwille, et al., 2004). In our driving
sim ulator exp erimen t, it w as found that 84.37% (27/32) of the participan ts p erformed
lik e what it is describ ed ab o v e.
Figure 4.11a sets an example of ho w driv er gaze b eha vior p erforms during a LC
course. The blue line represen ts the lateral p osition of the sub ject v ehicle. The green line,
whic h is a binary signal, represen ts driv er gaze b eha vior, i.e. glancing at the left-view
mirror. If the driv er glances at the left view mirror, the signal turns to 2 without unit
6
and if there is no glancing b eha vior it k eeps 0.
The red line represen ts the status of the left-turn signal also with a binary signal
with 0 for switc hing off and -2 for switc hing on. The inserted pictures are extracted from
soft w are BeGaze 3.6, whic h w ere recorded during the exp eriment. The green p oin ts in
the pictures are fixations of the participan t. By observing fixations, w e can clearly kno w
where the driv er is lo oking at during the en tire driv e.
Th us for eac h LLC case, based on driv er gaze b eha vior,
t pr epar e
can b e defined as the
last fixation on left view mirror (termed as mirror-glancing b eha vior) b efore the driv er
switc hing on the left-turn indicator. This case is illustrated b y the green rectangular
w a v e b efore the red one, in Figure 4.11a. The green rectangular w a v es far a w a y from
t 0
are not regarded as
t pr epar e
since they are just normal mirror-glancing b eha vior with no
follo wing LC maneuv er executed. Ho w ev er, in case that a LC whic h no mirror-glancing
b eha vior is detected b efore a left-turn signal, w e set the momen t of switching left-turn
indicator on as
t pr epar e
. This sp ecial case can b e illustrated in Figure 4.11b, where w e can
see that the driv er switc hes on the turn indicator but b efore it there is no mirror-glancing
b eha vior detected.
With the GBL metho d, time series data b et w een
t pr epar e
and
t chang e
are lab eled as
LC datasets. F or the problem of classification, b ecause of the unequal class distribution,
un balanced data sets ha v e commonly agreed that the p erformance of the classifiers tends
to b e biased to w ards the ma jorit y class (Gangan w ar, 2012). In order to obtain balanced
training datasets, the same amoun t of samples should b e lab eled as LK datasets as it is
depicted in Figure 4.11c, where LK data samples are lab eled righ t b efore
t pr epar e
and
the n um b er of the samples is equal to LC samples.
6
The reason of setting this v alue is just used for visualization, to matc h the v alue of the v ehicle
p osition represen ted in the blue line.
42

4.4 Lane-c hange data lab eling
𝑡 𝑝𝑟𝑒𝑝𝑎𝑟𝑒
𝑡 𝑐ℎ𝑎𝑛𝑔𝑒
𝑡 0
After Lane changing
Lane k eeping
m

(a)
Lane-c hange course recorded b y BeGaze 3.6. This case demonstrates the
driv er glances at the left-view mirror b efore switc hing on the left signal.
𝑡 𝑐ℎ𝑎𝑛𝑔𝑒
𝑡 𝑝𝑟𝑒𝑝𝑎𝑟𝑒

(b)
Lane-c hange course recorded b y BeGaze 3.6. This case demonstrates the
driv er glances at the left-view mirror after switc hing on the left signal.
Subject
vehicle
𝑡 0
LK dataset LC dataset
Slow
leading
vehicle
𝑡 𝑐ℎ𝑎𝑛𝑔𝑒
𝑡 𝑝𝑟𝑒𝑝𝑎𝑟𝑒

(c)
GBL metho d lab eling LC and LK datasets. The num b er of lab eled LC
samples and LK samples is equal.
Figure 4.11: Lab eling LC and LK data samples to attain balanced datasets.
43

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
4.4.2 Time-windo w lab eling metho d
𝑡 0
𝑡 𝑖𝑛𝑡 𝑒𝑛𝑡

(a)
Using a time-windo w to emp erically define
t intent
momen t.
Subject
vehicle
𝑡 0
LK dataset LC dataset
Slow
leading
vehicle
5𝑠
1 0𝑠

(b)
Lab eling LC and LK datasets using TWL with time-windo w
= 5 s.
Figure 4.12: Illustration of the TWL metho d.
In comparison to the GBL metho d, the pro cess of TWL metho d is illustrated in
Figure 4.12a. Instead of defining
t pr epar e
, the TWL metho d directly uses a time-windo w
with an ad ho c duration of time p erio d b efore
t 0
to lab el
t intent
. And then it selects a
b etter windo w size after ev aluation of the classification p erformance. T ak e the example of
using 5 s as the time-windo w, LC and LK datasets are lab eled as is sho wn in Figure 4.12b.
In order to k eep balance lab eling, time series data b et w een
t 0
and prior 5 s are lab eled
as LC class, while data b et w een 10 s and 5 s are lab eled as LK class.
4.5 Mo del implemen tation
In this section, w e in tro duce ho w to use a BN to mo del driv er LC b eha vior and ho w to
learn the parameters of the BN. A t the same time, the configuration of the SVM and
the naiv e Ba y es mo del are also detailed. In addition, the mo del ev aluation metho ds are
explained.
4.5.1 Ba y esian net w ork
Ba y esian net w ork (BN) is a directed acyclic graph net w ork and has sho wn its effectiv eness
in man y fields of prediction, e.g. parsing video ev en ts and agen ts’ in ten t prediction (P ei,
Jia, and Zh u, 2011), enem y’s tactical in ten tion prediction (Johansson and F alkman, 2006),
44

4.5 Mo del implemen tation
user’s clic k in ten t for w eb searc h ranking (Chap elle and Zhang, 2009) and prediction of
high w a y traffic maneuv ers (W eidl et al., 2018). Since driv er b eha vior could suffer from
v arious uncertain ties of driving con textual traffic and driver status, w e should design
an algorithm that is capable of dealing with suc h uncertain ties. T o this end, a BN is
in tro duced to offer an explicit, graphical, and in terpretable represen tation of uncertain
kno wledge (Bielza and Larrañaga, 2014). In addition, BN is compatible with man y kinds
of probabilit y inference, including tabular probabilit y , Gaussian distribution, soft-max
function, ro ot function, or Gaussian mixture distribution (Murph y, 1998a). Inspired
b y its extensiv e applications and effectiv eness, w e can use a BN to predict driv er LC
b eha vior that is able to consider driving uncertain ties 7 .
Lane-c hange Ba y esian net w ork
Y
LC
X
Figure 4.13:
Illustration of the lane-c hange Bay esian net w ork, where no de
X
and
Y
represen t the dynamic driving situation.
The directed acyclic graph of the lane-c hange Ba y esian net w ork (LCBN) is sho wn in
Figure 4.13. The square represen ts a discrete v ariable (
LC
is a binary state) and the
cycles represen t the con tin uous v ariables
X
and
Y
that con tain driving uncertain ties.
The meaning of v ariables in LCBN is detailed as follo ws:
• The relationship b et w een the sub ject v ehicle and the vehicle in cell f is
x = [ x 1 , ..., x t ..., x n ] T , (4.5)
where
x t
= [
ttc − 1
f t , tg ap − 1
f t
]
T
is the data p oin t at time
t
and
n
is the n um b er of data
p oin ts.
7
A Ba y es Net T o olb o x is used to create customized Bay esian net w orks. This T o olb o x is
based on Matlab dev elop ed b y Murph y (Murphy , 1998a), whic h can b e found in the link
h ttp://www.cs.utah.edu/ tc h/notes/matlab/bn t/do cs/usage.html (visited on 31.07.2019)
45

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
•
The relationship b et w een the sub ject v ehicle and the v ehicles in cell
f l
and cell
bl
is
y = [ y 1 , ..., y t ..., y n ] T , (4.6)
where
y t
= [
ttc − 1
f l t , ttc − 1
bl t
]
T
is the data p oin t at time
t
and
n
is the n um b er of data
p oin ts.
•
No de
LC
is a binary v ariable of represen ting whether the driv er intends to mak e
LLC:
LC = k , k ∈ { 0 , 1 } , (4.7)
with k = 0 for lane-k eeping and k = 1 for lane-c hanging.
In LCBN framew ork, w e set
X
no de at an upp er la y er and
Y
no de at the b ottom
la y er based on t w o facts:
1.
Whether the driv er w an ts to mak e LC highly dep ends on the traffic situation on
the curren t lane in fron t of the sub ject vehicle.
2. When the driv er mak es the final LC decision, the driv er w ould observ e the traffic
situation on the target lane to ensure safet y .
The LCBN mo del aims to infer the probabilit y of
LC
= 1 giv en the observ ations
X
and
Y (gra y circles), whic h can b e computed b y
P ( LC = 1 | X , Y ) . (4.8)
Based on Ba y es theorem, the p osterior probabilit y of making LC giv en the curren t
observ ations of driving situation is
P ( LC = 1 | X , Y ) = P ( Y | LC = 1 , X ) · P ( LC = 1 | X )
∑ 1
k =0 P ( Y | LC = k , X ) · P ( LC = k | X ) . (4.9)
In a BN, it is assumed that eac h v ariable is indep enden t of its non-descendan ts in the
graph giv en the state of its paren ts (F riedman, Geiger, and Goldszmidt, 1997). F or our
LCBN,
LC
is the paren t of
Y
, and
X
is non-descendan t of
Y
. Therefore, v ariables
X
and Y are conditional indep enden t to eac h other giv en LC , i.e.
P ( Y | LC = k , X ) = P ( Y | LC = k ) . (4.10)
46

4.5 Mo del implemen tation
And then equation (4.9) can b e written as
P ( LC = 1 | X , Y ) = P ( Y | LC = 1) · P ( LC = 1 | X )
∑ 1
k =0 P ( Y | LC = k ) · P ( LC = k | X ) . (4.11)
In order to compute the p osterior probabilit y
P
(
LC
=
k | X , Y
) in equation
(4.11)
, w e
need to estimate the conditional probabilit y distributions
P
(
LC
= 1
| X
) and
P
(
Y | LC
=
k
) . Since
LC
is a binary v ariable and
X
is a con tin uous v ariable, w e use a logistic
function to estimate P ( LC = 1 | X ) , giv en b y
P ( LC = 1 | X , β ) = 1
1 + e − β T X , (4.12)
where
β
is the parameter with the same dimension of
X
. If w e set
X
no de as a Gaussian
no de, it is tractable to inference
P
(
Y | LC
=
k
)
, k ∈ { 0 , 1 }
(Murph y, 1998b; Murph y,
1999; Murph y et al., 2001; Murph y, 2012).
In case
P
(
Y | LC
=
k
) do es not follo w a standard Gaussian distribution, it would
lead to p o or p erformance. Just lik e what is discussed in section 3.1.3, where the mo del
sho ws p o or fitting result if w e fit a data samples in Gaussian distribution ho w ev er it
do es not follo w Gaussian distribution.
F ortunately , previous researc h found that GMM has a go o d compatibilit y with BN
in fitting the probabilit y distribution of observ ations (Dielmann and Renals, 2004; Sun,
Zhang, and Y u, 2006). This giv es us clues to fit P ( Y | LC = k ) with GMM.
LCBN with GMM
Y
X
𝜃
LC
GMM
Figure 4.14: Illustration of LCBN incorp orated with GMM.
47

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
GMM is a parametric probabilit y densit y function represen ted as a w eigh ted sum of
Gaussian comp onen t densities (Reynolds, 2015; Bishop, 2006), giv en by
p ( x | w i , µ i , Σ i ) =
M
∑
i =1
w i N ( µ i , Σ i ) (4.13)
where
M
is the n um b er of mixture comp onen ts,
w i
is the w eigh t of
i th
Gaussian
distribution with 0
≤ w i ≤
1
, ∑ M
i =1 w i
= 1 , and
µ i , Σ i
are the mean and co v ariance of
i th
Gaussian distribution, resp ectiv ely . In order to in tegrate GMM in to LCBN, termed as
LCBN-GMM, w e extend the LCBN framew ork b y adding a new no de
θ
=
{ w i , µ i , Σ i } M
i =1
.
θ
is a hidden no de depicted as a dotted square, as is sho wn in Figure 4.14. Therefore,
the p osterior probabilit y can also b e written as
P ( LC = 1 | X , Y , θ ) = P ( X | LC = 1 , θ ) · P ( LC = 1 | X , θ )
∑ 1
k =0 P ( Y | LC = k , θ ) · P ( LC = k | X , θ ) , (4.14)
where
P ( LC = 1 | X , θ ) = P ( θ | LC = 1) · P ( LC = 1 | X )
∑ 1
k =0 P ( θ | LC = k ) · P ( LC = k | X ) (4.15)
and
p ( Y | LC = 1 , θ ) =
M
∑
i =1
w i N ( µ i , Σ i ) . (4.16)
Th us, the parameters of LCBN-GMM are ξ = [ β , θ ] .
P arameter estimation
In LCBN-GMM, giv en a dataset of observ ations
O
= [
o 1 , ..., o t ..., o N
]
T
, then their
lik eliho o d is computed b y
p ( O | ξ , LCBN − GMM) =
N
∏
i =1
p ( o i | ξ , LCBN − GMM) . (4.17)
And the log-lik eliho o d can b e th us written as
L ( ξ ) =
N
∑
i =1
l og ( p ( o i | ξ , LCBN − GMM)) . (4.18)
Due to the nonlinearit y with resp ect to the parameters, using maxim um-lik eliho o d
for estimation is not p ossible (Dempster, Laird, and R ubin, 1977). Here, the
exp ectation-maximization (EM) algorithm is used to estimate h yp er-parameters in
48

4.5 Mo del implemen tation
LCBN-GMM (Ghahramani, 1998; Bishop, 2006). The iteration pro cedure of the EM is
illustrated in section 3.2.
Before training LCBN-GMM, w e need to determine the parameter
M
in equa-
tion (4.13) based on the Ba y esian information criterion (BIC) (Caner, 2009):
B I C = − 2 ∗ L ( ξ ) + p ∗ l og ( q ) (4.19)
where
L ( ξ )
is the estimated log-lik eliho o d in equation
(4.18)
,
q
= 2 is the amoun t
of observ ations in LCBN-GMM with t w o observ ations, and
p
= 4 is the amoun t of
estimated n um b er of parameters in GMM. Generally , a lo w er BIC v alue represen ts b etter
fitting result (Steele and Raftery, 2010). Figure 4.15 sho ws the tendency of BIC v alues
with resp ect to the n um b er of GMM comp onen ts presen ted b y driv ers with different
driving st yles. It indicates that BIC is mainly con v ergen t at
M
= 9 . Th us, w e set
M = 9 in for LCBN-GMM.
2 4 6 8 10 12 14
M
-1
-0.5
0
0.5
1
1.5
2
2.5
BIC
× 10 4
aggressive drivers
neural drivers
conservative drivers

Figure 4.15: BIC v alues of three driving st yles with resp ect to parameter M .
4.5.2 Other mac hine learning mo dels
Besides BN, SVM and naiv e By es (NB) are also p opular mac hine learning mo dels for
classification. F or instance, SVM is used for driv er fatigue prediction (Senaratne et al.,
2007; Camlica, Hilal, and Kulić, 2016), stress ev ent prediction (Rigas, Goletsis, and
F otiadis, 2011) and naiv e Ba y es classifier for driv er LC in ten t prediction (Lethaus,
Baumann, et al., 2013). Thus, these t w o mo dels are also implemen ted in this c hapter
for further ev aluation.
Unlik e BN, whic h is v ery complex to mo del the net w ork and learn the parameters,
the implemen tation of SVM and NB is simpler. The non-linear k ernel function used
in SVM to generate decision b oundary is a Gaussian k ernel recommended b y Ben-Hur
and W eston (2010) and W ang et al. (2017). And w e also map the prior distribution of
NB in Gaussian distribution for simplicit y . Both of them can b e implemen ted using the
49

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
Statistics and Mac hine Learning T o olb o x
8
b y Matlab. All the mo dels are trained b y
exactly the same training datasets.
4.5.3 Mo del training and ev aluation metho d
All the data w ere firstly group ed in to three categories according to driving st yles (high
aggressiv e, medium aggressiv e, and lo w aggressiv e) and again separated them b y three
pre-defined driving scenarios illustrated in Figure 4.1. Th us, training datasets are
organized b y driving st yles and driving scenarios. In addition, the set of all the lab eled
data samples that are simply group ed together without doing an y separation is termed
as non-c ate gorize d gr oup . T otally , the num b er of lab eled data samples b y the GBL
metho d for eac h LC scenario and driving st yle are listed in T able 4.1.
T able 4.1: The n umber of lab eled data samples using the GBL metho d.
Aggressiv eness T yp e Scenario Scenario Scenario
lead only lead + adjacen t b ehind lead + 2 adjacen t
High LC 1280 2620 2502
LK 1280 2620 2502
Medium LC 2271 4789 4431
LK 2271 4789 4431
Lo w LC 1245 2214 2145
LK 1245 2214 2145
Non-categorized LC 4796 9623 9078
LK 4796 9623 9078
In order to guaran tee that the training data and testing data are disjoin t, a cross-
v alidation (CV) metho d is used to ev aluate the mo dels. Eac h lab eled dataset is randomly
and ev enly divided in to ten folds. Nine folds are used for training and the rest one fold
is used for testing. T otally , suc h pro cedure is conducted ten times.
Receiv er op erating c haracteristic (R OC) curv e (McCall et al., 2007; Morris, Doshi,
and T riv edi, 2011) is used to access the p erformance of the mo dels. It is already
explained in section 3.3.1. W e plot R OC curv es for eac h mo del using the function
pro vided b y Matlab
9
, b y whic h giv en true class lab els of the testing samples and the
predicted confidence, true negativ es, false p ositiv es, and false negativ es as w ell as A UC
v alues can b e calculated. Classification p erformance can b e ev aluated based on A UC
v alues (Huang and Ling, 2005; Vic k ers and Elkin, 2006).
8 h ttps://www.math w orks.com/pro ducts/statistics.html (visited on 31.07.2019)
9 h ttps://www.math w orks.com/help/stats/p erfcurve.h tml (visited on 31.07.2019)
50

4.6 Result and analysis
4.6 Result and analysis
Statistics of the lab els b y GBL metho d
Firstly w e mak e a statistical analysis to see if
t pr epar e
is v arying b y driving st yles as
w ell as b y driving scenarios. The statistics of
t pr epar e
(duration b et w een
t pr epar e
and
t 0
) for the three driving st yles in the differen t scenarios depicted in b o x plot is sho wn
in Figure 4.16. The mean and standard deviation (SD) are listed in T able 4.2. The
follo wing results can b e found:
•
F rom the p ersp ectiv e of driving st yle: on a v erage,
t pr epar e
of high aggressiv e driv er
is smaller than less aggressiv e driv er (medium and lo w). This result means that
high aggressiv e driv er tak es shorter time to prepare for a LC.
•
Comparison of differen t LC scenarios: it indicates that the mean of
t pr epar e
in
Sc enario le ad only is smaller than in other t w o scenarios. In addition, the mean of
t pr epar e
increases as the scenario b ecomes more complex (complexit y: Sc enario le ad
only < Sc enario le ad + adjac ent b ehind < Sc enario le ad +2 adjac ent ). The results
are also consisten t with what is concluded in Beggiato et al. (2018) that the more
v ehicles on the target lane, the more and longer mirror glances.
Th us, it can b e concluded that driving st yle and con textual traffic do impact on
lab eling results, whic h is coincide with the reference b y Rehder et al. (2016).
High Medium Low
Scenario
lead only
1
2
3
4
5
6
7
8
Seconds
High Medium Low
Scenario
lead + adjacent behind
2
3
4
5
6
7
8
High Medium Low
Scenario
lead + 2 adjacent
2
3
4
5
6
7
8

Figure 4.16:
The b o x plots of the lab eled momen t
t pr epar e
b efore
t 0
for differen t scenarios
and differen t levels of aggressiv e driving st yles.
51

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
T able 4.2:
The mean and SD (in second) of the lab eled
t pr epar e
b efore momen t
t 0
for
differen t scenarios and driving styles.
Aggressiv eness Scenario Scenario Scenario
lead only lead + adjacen t b ehind lead + 2 adjacen t
High 3.53 (SD = 1.06) 3.88 (SD = 1.35) 4.06 (SD = 1.60)
Medium 3.80 (SD = 1.51) 4.31 (SD = 1.53) 4.57 (SD = 1.44)
Lo w 3.98 (SD = 1.45) 4.89 (SD = 1.22) 4.45 (SD = 1.30)
Mean 3.76 (SD = 1.38) 4.31 (SD = 1.46) 4.40 (SD = 1.46)
4.6.1 Comparison b et w een differen t mo dels
T able 4.3: The A UC v alues p erformed b y differen t mo dels with GBL.
Scenario Aggressiv eness LCBN-GMM NB SVM
lead only
High 0.92 ( ↑ ) 0.90 ( ↓ ) 0.88 ( ↓ )
Medium 0.94 ( ↑ ) 0.95 ( ↑ ) 0.91 ( ↑ )
Lo w 0.93 ( ↑ ) 0.90 ( ↓ ) 0.91 ( ↑ )
Mean 0.93 ( ↑ ) 0.92 ( ↑ ) 0.90
Non-categorized 0.89 0.91 0.90
lead + adjacen t b ehind
High 0.91 ( ↑ ) 0.92 ( ↑ ) 0.93 ( ↑ )
Medium 0.90 ( ↑ ) 0.88 ( ↑ ) 0.90 ( ↑ )
Lo w 0.93 ( ↑ ) 0.83 ( ↑ ) 0.84 ( ↓ )
Mean 0.91 ( ↑ ) 0.87 ( ↑ ) 0.89 ( ↑ )
Non-categorized 0.86 0.85 0.87
lead + 2 adjacen t
High 0.89 ( ↑ ) 0.83 ( ↑ ) 0.82 ( ↑ )
Medium 0.86 ( ↑ ) 0.63 ( ↓ ) 0.67 ( ↓ )
Lo w 0.88 ( ↑ ) 0.72 ( ↑ ) 0.82 ( ↑ )
Mean 0.87 ( ↑ ) 0.73 ( ↑ ) 0.77 ( ↑ )
Non-categorized 0.74 0.71 0.70
In this section, mo del comparison is made b et w een LCBN-GMM, SVM and NB
using the GBL metho d. A UC v alues of the classification (LC and LK samples)
p erformed b y differen t mo dels and using differen t training datasets are listed in T able 4.3.
The corresp onding R OC curv es can b e found in App endix Figure A.2 – Figure A.4.
Considering eac h LC scenario alone, the highest A UC v alue ac hiev ed b y certain mo del
(horizon tal comparison) is mark ed as b old. W e can see that on a v erage (see the
mean), LCBN-GMM p erforms b est. And it almost ac hiev es the b est p erformance with
52

4.6 Result and analysis
eac h driving st yle training dataset and in eac h LC scenario. The only exception are
in Sc enario le ad only and Sc enario le ad + adjac ent b ehind , where Naiv e Ba y es with
medium aggressiv e driving st yle datasets and SVM with high aggressiv e driving st yle
datasets p erform sligh tly b etter than LCBN-GMM.
In conclusion, considering the a v erage A UC scores, LCBN-GMM outp erforms SVM
and NB.
4.6.2 Comparison b et w een training datasets
In this section, comparison is made b et w een differen t training datasets, i.e. the high
aggressiv e, medium aggressiv e, lo w aggressiv e as w ell as the non-categorized dataset.
W e compare A UC v alues b et w een the categorized datasets and non-categorized dataset
with an up arro w ‘
↑
’ for p erformance impro v emen t and a do wn arro w ‘
↓
’ for p erformance
deterioration (v ertical comparison).
F rom T able 4.3 it can b e seen that using the non-categorized datasets, the three
mo dels ac hiev e similar classification results in each scenario, e.g. from 0.89 to 0.91 in
Sc enario le ad only , from 0.85 to 0.87 in Sc enario le ad + adjac ent b ehind , and from 0.70
to 0.74 in Sc enario le ad + 2 adjac ent .
The a v erage p erformance can b e impro v ed if the mo del is trained b y the categorized
datasets in comparison to the non-categorized datasets. The only exception is SVM
in Sc enario le ad only , where the a v erage p erformance is the same as using the non-
categorized dataset. The classification results in differen t scenarios will b e discussed
separately as follo ws.
•
In Sc enario le ad only : using the categorized datasets the p erformance can b e
sligh tly impro v ed from 0.89 to 0.93 for LCBN-GMM and from 0.91 to 0.92 for
Naiv e Ba y es. SVM is the exception.
•
In Sc enario le ad + adjac ent b ehind : using the categorized datasets rather than
the non-categorized datasets, mo del p erformance can b e impro v ed for all metho ds.
More sp ecifically , mo del p erformances of LCBN-GMM, Naiv e Ba y es and SVM are
impro v ed from 0.86 to 0.91, from 0.85 to 0.87, and from 0.87 to 0.89, resp ectiv ely .
But for the lo w aggressiv e driving st yle dataset, the p erformance of SVM sligh tly
decreases compared with using the non-categorized dataset.
•
In Sc enario le ad +2 adjac ent : w e find that compared with using the non-categorized
datasets, mo del p erformance can b e impro v ed b y using the categorized datasets for
all metho ds. F or instance, A UC v alue increases from 0.74 to 0.87 for LCBN-GMM,
from 0.71 to 0.73 for Naiv e Ba y es, and from 0.70 to 0.77 for SVM. It also indicates
that for Naiv e Ba y es and SVM, the p erformance of using the medium aggressiv e
driving st yle datasets do es not ha v e significan t impro v emen t.
It can b e concluded that mo del p erformance can b e impro v ed b y using the categorized
training datasets compared with using the non-categorized datasets.
53

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
4.6.3 Comparison b et w een the lab eling metho ds
T able 4.4:
The mean and SD of the A UC v alues p erformed b y LCBN-GMM with differen t
lab eling metho ds.
Lab el t yp e Scenario Scenario Scenario
lead only lead + adjacen t b ehind lead + 2 adjacen t
GBL 0.93 (SD = 0.008) 0.91 (SD = 0.012) 0.87 (SD = 0.012)
TWL 5 s 0.85 (SD = 0.049) 0.82 (SD = 0.057) 0.72 (SD = 0.054)
TWL 4 s 0.90 (SD = 0.059) 0.85 (SD = 0.054) 0.80 (SD = 0.043)
TWL 3 s 0.92 (SD = 0.070) 0.87 (SD = 0.054) 0.78 (SD = 0.063)
TWL 2 s 0.94 (SD = 0.057) 0.91 (SD = 0.051) 0.82 (SD = 0.066)
TWL 1 s 0.95 (SD = 0.040) 0.96 (SD = 0.004) 0.87 (SD = 0.052)
In this section, comparison is made b et w een the GBL and the TWL metho d p erformed
b y LCBN-GMM. T able 4.4 summarizes the mean and standard deviation (SD) of A UC
v alues. The full A UC v alues of TWL can b e seen in T able A.1 in App endix.
It indicates that only b y c ho osing time-windo ws that are less than 2 s in Sc enario
le ad only and time-windo w of 1 s in Sc enario le ad + adjac ent b ehind , the TWL metho d
could p erforms sligh tly b etter than the GBL metho d. In Sc enario le ad +2 adjac ent , the
GBL metho d represen ts a smaller SD of A UC v alues compared with TWL metho d. F or
instance in Sc enario le ad only , SD of A UC v alues for the GBL metho d is 0.008, whic h is
m uc h smaller than that of the TWL metho d. F rom this p ersp ectiv e, the GBL metho d
ac hiev es more stable p erformance than the TWL metho d, except for Sc enario le ad +
adjac ent b ehind when TWL c ho oses 1 s as the time-windo w.
In practice, the TWL metho d w orks lik e this: firstly c ho osing differen t time-windo ws
and selecting the b est time-windo w size after ev aluation lik e what w e listed in T able 4.4.
Ho w ev er, this pro cess is inefficien t. The selected time-windo w size is v ery scenario
sp ecific, whic h means it do es not co v er general situation. In comparison, the GBL
metho d tak es adv an tages of driv er gaze b eha vior, it can find t pr epar e in eac h LC ev en ts
with unified lab eling criteria, and th us it can mak e sure that the datasets are lab eled
in high qualit y . In conclusion, considering efficiency the GBL metho d outp erforms the
TWL metho d.
4.6.4 Real-time lane-c hange b eha vior prediction
In order to further ev aluate the GBL and the TWL metho d, a real-time LC b eha vior
prediction test is made b y feeding time series driving data to the well-trained model.
Since LCBN-GMM represen ts go o d result in mo del comparison, w e use this mo del to
p erform the test. Prediction precision and predicted time in adv ance of LC b eha vior are
54

4.6 Result and analysis
as metrics to ev aluate these t w o lab eling metho d. F or the TWL metho d, time-windo w
size is c hosen as 2 s time-windo w in Sc enario le ad only , and 1 s time-windo w in Sc enario
le ad + adjac ent b ehind and Sc enario le ad +2 adjac ent . The reason of c ho osing such
time-windo ws is that in T able 4.4 these time-windo ws are with highest A UC v alues in
eac h driving scenario.
Data used for the real-time test are collected from the exp erimen t describ ed in
section 4.3, including totally 304 LC cases. During test, if the output LC probabilit y of
LCBN-GMM is greater than a predefined threshold, then a rep ort of LC in ten t will b e
giv en, otherwise not. The rule of determining whether it correctly predicts driv er LC
b eha vior is defined as: if the mo del predicts a LC at certain momen t and in the follo wing
8 seconds the driv er dose execute a LC maneuv er, then this prediction is regarded as
correct, otherwise incorrect. Here a threshold of 8 s is selected since the largest time
in terv al b et w een
t pr epar e
and
t 0
w e lab eled is close to 8 s. The statistics of
t pr epar e
can
b e seen in Figure 4.16.
T able 4.5:
The real-time prediction result p erformed b y LCBN-GMM using GBL and
TWL metho d.
T yp e
Lab el Scenario Scenario Scenario
Mean
metho d lead only lead + lead +
adjacen t b ehind 2 adjacen t
Precision GBL 56 (46) 82.1% 124 (89) 71.7% 124 (102) 82.2% 78.2%
TWL 56 (50) 89.2% 124 (89) 71.7% 124 (45) 36.2% 60.7%
Prediction GBL 4.5 (SD = 1.5) 4.8 (SD = 1.7) 4.1 (SD = 1.4) 4.5
time (s) TWL 4.7 (SD = 1.9) 4.5 (SD = 1.7) 4.0 (SD = 2.4) 4.4
The final prediction results are listed in T able 4.5, and the b o x plot can b e seen
in Figure 4.17. T able 4.5 suggests that only in Sc enario le ad only , the TWL sligh tly
outp erforms the GBL metho d with earlier prediction time and higher precision. In
Sc enario le ad + adjac ent b ehind and Sc enario le ad +2 adjac ent , ho w ev er, the GBL
metho d p erforms m uc h b etter than the TWL with b oth earlier prediction time and
higher precision. Esp ecially in Sc enario le ad +2 adjac ent , the TWL metho d gets p o or
precision of only 36.2%. How ev er, the GBL metho d can ac hiev e 82.2%. In addition,
considering the a v erage p erformance, the GBL metho d is still with higher accuracy ,
earlier prediction time as w ell as smaller SD.
In conclusion, the GBL metho d outp erforms the TWL metho d with higher precision
and earlier predicted time.
Figure 4.18 giv es an example of how LCBN-GMM predicts driv er LC b eha vior in
real-time case. The blue line represen ts the lateral p osition of the sub ject v ehicle. W e
can see that the driv er c hanges to the left lane at around 30 s and steers bac k to the
55

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
lead only lead + adjacent behind lead + 2 adjacent
2
4
6
8
s

(a) The GBL metho d
lead only lead + adjacent behind lead + 2 adjacent
0
2
4
6
8
s

(b) The TWL metho d
Figure 4.17: The b o x plot of correctly predicted LC b efore t 0 .
𝑡 0 𝑡 0
𝑡 𝑖𝑛𝑡 𝑒𝑛𝑡
𝑡 𝑖𝑛𝑡 𝑒𝑛𝑡

Figure 4.18: An example of the real-time prediction p erformance of LCBN-GMM.
56

4.7 Summary
righ t lane at around 40 s. The pink line is the p osterior probabilit y of LC b eha vior
b y LCBN-GMM. The probabilit y threshold of giving a LC rep ort is set as 0.9. That
is to sa y if the probability is greater than 0.9, then LCBN-GMM w ould rep ort a LC
prediction rep ort. The green dotted line represen ts driv er mirror glancing b eha vior,
whic h is a binary signal with v alue of 2 for observing a glancing and of 0 for no glancing
b eha vior. Because the signal of driv er mirror glancing b eha vior is only used for data
lab eling rather than an input of LCBN-GMM, the normal glancing signals do not impact
on LC prediction probabilit y . It can b e seen that ev en there are t w o mirror glancing
b eha viors observ ed b et w een 0 and 10 s, LCBN-GMM do es not rep ort LC. The driv er
executes the first LC maneuv er at around 30 s and LCBN-GMM can predict it sev eral
seconds ahead of the actual LC maneuv er. LCBN-GMM is mo deled only for left LC
case, ho w ev er, for righ t LC case the output probabilit y of LCBN-GMM is sta y in 0.
4.7 Summary
This c hapter prop oses a framew ork of prediction of driv er LC b eha vior including w orks
lik e the design of driving sim ulator-based exp erimen t, feature extraction, mo deling
mac hine learning mo dels, training datasets preparation, mo del selection and ev aluation.
Inspired b y the prior researc h, sev eral impro v emen ts ha v e b een made e.g. considering
driving con textual traffic and driving st yles in preparing training datasets, prop osing a
gaze-based lab eling metho d (GBL) to obtain high qualit y training datasets.
In conclusion, comparison of differen t ML mo dels, result indicates that LCBN-
GMM p erforms b etter than SVM and NB. Comparing differen t training datasets, the
p erformance of the mo dels trained b y categorized datasets outp erform b y non-categorized
training datasets. This applies to all the mo dels in eac h driving scenario. In addition,
comparison of the GBL and the TWL metho d, the GBL outp erforms the TWL with
b oth higher precision and earlier predicted time. Finally , using the GBL metho d LCBN-
GMM ac hiev es 78% prediction precision and could predict driv er LC b eha vior nearly
4.5 seconds ahead of an actual LC maneuv er.
The limitation of the study is that the exp erimen t w as conducted in a driving
sim ulator, whic h means the exp erimen t condition is to ol idealized compared with
the real-road traffic and th us the participan ts tend to b e absen t of real risk while
driving (Sc hmitt et al., 2018). In addition, during the exp erimen t the participan ts w ere
giv en oral instruction b efore doing driving task. This ma y impact on the driving habits
of the participan ts. F or example, b efore the exp erimen t, participan ts w ere suggested to
use the turn signal b efore taking a left LC, ho w ev er, the case of no turn signal usage
during LC course migh t happ en in real situation. F urthermore, the metho d used for
classification of driving st yles is limited b y the questionnaire. The driving aggressiv eness
score tends to describ e the driv er’s driving st yle globally , i.e. from p ersonalit y trait
lev el, ho w ev er, t w o driv ers who score similarly ma y sho w differen t temp orary driving
57

4. Exp erimen t 1 - Prediction of driv er lane-c hange b eha vior based on a driving
sim ulator exp erimen t
st yles whic h can b e represen ted b y differen t sp eed c hoice as w ell as accelerating or
braking b eha vior etc.. This limitation also happ ens to all the metho ds that attempt to
classify the driv er’s global driving st yle (Sagb erg et al., 2015). Th us, seeking to consider
individual driving st yle in preparing for datasets w ould b e one solution. A dditionally ,
there is no systematic feature selection w ork b efore mo del training. That is to sa y ,
researc h should b e done to test whether the selected features are really suitable or
not. Most of the feature selection w ork b y previous researc h is based on the empirical
kno wledge. Therefore, a comprehensiv e study of feature selection on driv er LC b eha vior
is needed.
58

5
Big data analysis - Evaluation of
feature selection fo r driver lane-change
b ehavio r
©
Xiaohan Li (the author of this dissertation), W ensh uo W ang, Zhang Zhang and
Matthias Rötting. Reprin ted from, Effects of feature selection on lane-c hange maneuv er
recognition: an analysis of naturalistic driving data, Journal of In telligen t and Connected
V ehicles, published b y Emerald Publishing Limited, Octob er 2018. DOI: 10.1108/JICV-
09-2018-0010.
A substan tial p ortion of this c hapter is based on the ab o ve paper.
5.1 In tro duction
A t the end of c hapter 4, it prop oses the underlying problem of extracting features
based on the empirical kno wledge. In addition, another asp ect that can b e impro v ed in
the whole framew ork of driv er LC prediction is using naturalistic driving data instead
of using data collected from the driving sim ulator. In this c hapter, w e tend to mak e
impro v emen t in these t w o asp ects.
A ctually , the imp ortance of feature selection has b een emphasized on for a long
time in the field of mac hine learning (ML). F or instance, Menze et al. (2009) prop osed
a metho d of feature selection for classification of sp ectral data; Kira and Rendell
(1992) put forw ard a practical approac h of feature selection. V afaie and Imam (1994)
summarized t w o feature selection metho d i.e. genetic algorithms and greedy-lik e searc h.
Ho w ev er, sp ecifically for driv er LC b eha vior, there are few related w orks regarding to this
topic. Blindly using features without selection ma y lead to excessiv e computation time,
ho w ev er, insufficien t feature selection ma y cause p o or classification results. Selecting
59

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
Ego
vehicle
Object
vehicle
He is too
slow , I want to
overtake him !

(a) LC for the puropse of o v ertaking a slow leading v ehicle.
Ego
vehicle
The road is
going to the
end, I should
change lane.
Object
vehicle

(b) LC due to a lane drop.
Figure 5.1: An example of LC with t w o d ifferen t purp oses.
high con tributiv e features to classify lane-c hange (LC) and lane-k eep (LK) data samples
is necessary for further LC prediction w ork.
Man y kinds of features ha v e b een used for driv er LC b eha vior classification. F or
instance, longitudinal features lik e time to collision (Siv araman and T riv edi, 2014;
Liebner et al., 2013; P eng et al., 2015), longitudinal acceleration, and lateral features
e.g. steering angle (Xu et al., 2012), y a w rate (Siv araman and T riv edi, 2014; Doshi,
Morris, and T rivedi, 2011), and lateral acceleration (Kasp er et al., 2012; Boub ezoul,
K oita, and Dauc her, 2009). These features are assumed to b e strong enough for LC
b eha vior classification either based on in tuition or the empirical kno wledge, ho wev er,
this assumption is still hanging on and y et comprehensiv ely studied.
In general, driv er LC b eha vior can b e either discretionary or mandatory . A mandatory
lane-c hange will o ccur when a driv er m ust lea v e a lane due to a lane drop or b ypass a
blo c kage etc. A discretionary lane c hange o ccurs when a driv er prefers a more efficien t
adjacen t lane (J2944, 2013), e.g. when passing a slo w-mo ving leading v ehicle to main tain
the curren t sp eed (Lee, Olsen, Wierwille, et al., 2004). This means that in discretionary
and mandatory LC cases, the imp ortance of the features is differen t as w ell. In other
w ords, certain feature is useful to discretionary LC do es not mean it is also useful to
mandatory LC. T ak e the feature TTC for instance. Though t it is assumed to b e v ery
imp ortan t for mo deling LC decision-making pro cess (Lee, Olsen, and Wierwille, 2003;
60

5.2 Related w ork of feature selection
Nilsson and Sjöb erg, 2013), the imp ortance of TTC in the t w o differen t scenarios is
differen t. As w e can see in Figure 5.1, TTC can b e an imp ortan t feature for o v ertaking
purp ose (e.g. Figure 5.1a), ho w ev er, it ma yb e not that imp ortan t to mandatory LC (e.g.
Figure 5.1b). Leonhardt and W anielik (2017) ev aluated the effects of v arious features
in differen t LC driving scenarios. The result indicated that ev en for the same feature,
the w eigh t of o v ertaking a slo w v ehicle and merging is differen t. Th us, feature selection
pro cess should also tak e LC scenario in to account.
T o this end, in this c hapter w e in v estigate the con tribution of eac h extracted feature
from the p ersp ectiv e of statistics based on naturalistic driving data. The aim is to
comprehensiv ely figure out the imp ortance of differen t t yp es of features regarding to
driv er LC b eha vior and select the most con tributiv e features that can b e used for mo del
training. The main impro v emen ts in this study compared with the prior researc h can
b e summarized as follo ws:
1.
Presen t a feature selection metho d from the p ersp ectiv e of statistics to in v estigate
the statistical significance of eac h feature that is related to driver LC behavior.
2.
Not only time-domain features but also frequency-domain features are considered
to fill in gaps in the previous w orks. This can largely enric h the candidate feature
sets.
3.
In feature extraction pro cedure, differen t driving scenarios are tak en in to accoun t
to comprehensiv ely ev aluate the extracted features.
5.2 Related w ork of feature selection
The goal of feature selection is to reduce the dimension of the feature sets b y remo ving
unimp ortan t features. In general, feature selection metho d can b e group ed in to filter
metho d and wrapp er metho d. Filter metho d analyzes the in trinsic prop erties of data,
ranking and selecting features without in v olving learning algorithms. In comparison,
wrapp er metho d in v olv es learning algorithms. It w ould giv e scores to a giv en subset of
features based on certain algorithm (Guy on et al., 2008; Geng et al., 2007). F or wrapp er
metho d, the ranking of features can b e v arying from learning algorithm to algorithm.
This study is aim at finding the in trinsic prop erties of the features and selecting the
most con tributiv e features among all features rather than ranking and selecting features
for a sp ecific ML algorithm. Therefore, the feature selection metho d in this c hapter
b elongs to wrapp er metho d.
F or LC b eha vior prediction, the data collected from sensors are in the form of
time series, so the prop erties of the features in time-domain are the most frequen tly
extracted (Xu et al., 2012; Kasp er et al., 2012; Liebner et al., 2013; Siv araman and
T riv edi, 2014; P eng et al., 2015; Doshi, Morris, and T riv edi, 2011; Boub ezoul, K oita,
and Dauc her, 2009). On the other hand, frequency-domain features ha v e already b een
61

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
used to recognize driv er state. F or instance, the p ow er sp ectrum features via w a v elet
transform are selected for Belief net w orks (Ha jinoro ozi et al., 2015; Chen et al., 2015).
In other areas regarding to recognition suc h as sp eec h recognition (Thomas, Ganapath y,
and Hermansky, 2008) and anomaly detection (Zhang et al., 2008), frequency-domain
features pla y imp ortan t roles.
In this study w e consider the prop erties of the features in b oth time-domain and
frequency-domain.
5.3 Lane-c hange scenario mo deling
Ego
vehicle
𝑐𝑒𝑙𝑙 𝑙
𝑐𝑒𝑙𝑙 𝑚
𝑐𝑒𝑙𝑙 𝑟
𝑠 2
∗
𝑠 1
∗
𝑠 3
∗

(a) Illustration of LC o ccupancy sc hedule grid.
𝑅
Ego
vehicle
𝑅

𝑅 𝑟
𝑅 𝑟

𝑅 𝑙
𝑅 𝑙

𝑣𝑧 Ob ject
vehicles
𝑎𝑧
V ehicle 2
V ehicle 1
V ehicle 3
x
z

(b)
The relationship b et w een the ego v ehicle and ob ject
v ehicles.
Figure 5.2:
Illustration of the parameters used for calculating the length of the cell-grid.
T o easily describ e the relationship b et ween the ego v ehicle and its surroundings
v ehicles, w e adopt the metho d presen ted in section 4.2.1, i.e cell-grid based mo deling
metho d. Based on Do et al. (2017), totally 9 cells and 32 situations ( 2
5
) are considered
in the driving con textual traffic. But the pap er do es not giv e the sp ecific b oundary
of the cells. Kasp er et al. (2012) mo deled the cell b y considering the sp eed-dep enden t
information when a cell will b e o ccupied or will b ecome free. But they assumed that
the v ehicle can mo v e unobstructed tow ards certain cell, whic h cannot b e satisfied in
the situation where the ego v ehicle is b eing o v ertak en b y other v ehicles. In this c hapter
62

5.3 Lane-c hange scenario mo deling
w e aim to detail the cell-grid metho d b y considering the dynamic relationship b et w een
the ego v ehicle and the surrounding v ehicles. A 3-cell grid is emplo y ed to mo del the
con textual traffic for b oth left and righ t LC case with totally 8 scenarios.
The exp erimen tal v ehicle, from which the data used in this study w ere collected,
has no bac k view sensors installed, so only the fron t traffic can b e detected. The traffic
situation on the bac k of the ego v ehicle is not considered. Despite suc h limitation, the
metho d of mo deling con textual traffic in this c hapter can b e extended to more cell-grids
whic h can co v er the traffic on bac k of the ego v ehicle as w ell. But it is not within the
scop e of this study . Figure 5.2a depicts the mo deled cell-grid.
W e adopt the theory presen ted b y Karim et al. (2013) to define the middle cell (
cell m
)
and the theory b y Kesting and T reib er (2013) to define the left (
cell l
)/righ t cell (
cell r
).
The dynamic length of eac h cell is
s ∗
1
,
s ∗
2
,
s ∗
3
, resp ectiv ely , as it is sho wn in Figure 5.2a.
The length of
cell m
is defined b y a Mean Safe Time Gap (MSTG) based on Karim et al.
(2013) as:
MSTG = B T E V − B T O V + RT (5.1)
where
B T E V
and
B T O V
are the brak e time of the ego v ehicle and ob ject v ehicle 1,
resp ectiv ely .
RT
is the driv er’s p erception-reaction time. And for certain v ehicle, the
B T is calculated b y an empirical equation
B T = 0 . 02321 · v z − 0 . 08785 , (5.2)
where v z is the v ehicle sp eed and th us
B T E V − B T O V = 0 . 02321 · ˙
R (5.3)
where,
˙
R
is the range rate b et w een the ego v ehicle and ob ject vehicle 1. So, the dynamic
length of s ∗
1 can b e written as
s ∗
1 = v · MSTG , (5.4)
where v is the longitudinal sp eed of the ego v ehicle.
W e define
cell l
and
cell r
based on the In telligen t Driv er Mo del (IDM) (Kesting and
T reib er, 2013). In the study , the safe distance is deriv ed from the leading v ehicle,
driving at a desired sp eed, or preferring accelerations to b e within a comfortable range.
A dditionally , kinematical asp ects are tak en in to accoun t, suc h as the quadratic relation
b et w een braking distance and sp eed. Firstly , on the left and righ t lane, a term desir e d
distanc e on the left ( s ∗
l ) and righ t ( s ∗
r ) lane are defined resp ectiv ely as:
s ∗
l = s 0 + max (0 , v · T + ˙
R l · R l
2 · √ a ∗ · b ∗ ) (5.5)
63

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
s ∗
r = s 0 + max (0 , v · T + ˙
R r · R r
2 · √ a ∗ · b ∗ ) , (5.6)
where
s 0
is the minim um (bump er-to-bump er) gap,
T
is the safe time gap,
a ∗
and
b ∗
are
acceleration and comfortable deceleration.
R l
,
˙
R l
and
R r
,
˙
R r
are the range and range
rate the ego v ehicle with ob ject v ehicle 2 and v ehicle 3 in Figure 5.2b, resp ectiv ely . The
dynamic term
˙
R l · R l /
(2
· √ a ∗ · b ∗
) and
˙
R r · R r /
(2
· √ a ∗ · b ∗
) imply the in telligen t braking
strategy for left lane-c hange(LLC) and righ t lane-c hange (RLC) cases.
Secondly , based on the desir e d distanc e on the left (
s ∗
l
) and righ t (
s ∗
r
) lane , the
dynamic safet y distance, namely the length of s ∗
2 and s ∗
3 can b e written as:
s ∗
2 = s ∗
l
√ ( s ∗
l
R l ) 2 − ∆ a + a bias
az
(5.7)
s ∗
3 = s ∗
r
√ ( s ∗
r
R r ) 2 − ∆ a − a bias
az
, (5.8)
where
az
is the longitudinal acceleration of the ego v ehicle. ∆
a
is the LC threshold.
a bias represen ts the asymmetric prop ert y of LLC and RLC.
T able 5.1: The reference v alues regarding to the parameters for the cell grid
P arameter V alue
RT 1.9 s
T 1.0 s
s 0 2 m
a ∗ 1.0 m / s 2
b ∗ 1.5 m / s 2
∆ a 0.1 m / s 2
a bias 0.3 m / s 2
All the v alues of the parameters in equation
(5.1)
and equation
(5.5)
– equation
(5.8)
are listed in T able 5.1, and the o ccupancy states of cells can b e giv en b y





cell m = 0 if R ≥ s ∗
1
cell m = 1 if R < s ∗
1
(5.9)





cell l = 0 if R l ≥ s ∗
2
cell l = 1 if R l < s ∗
2
(5.10)
64

5.3 Lane-c hange scenario mo deling





cell r = 0 if R r ≥ s ∗
3
cell r = 1 if R r < s ∗
3
(5.11)
LLC Scenario 0_0 LLC Scenario 0_1 LLC Scenario 1_0 LLC Scen ario 1_1
𝑐𝑒𝑙𝑙 𝑙 𝑐𝑒𝑙𝑙 𝑚

(a) Left lane-c hange scenarios.
RLC Scenario 0_0 RLC Scenario 0_1 RLC Scenario 1_0 RLC Scenar io 1_1
𝑐𝑒𝑙𝑙 𝑟
𝑐𝑒𝑙𝑙 𝑚

(b) Righ t lane-c hange scenarios.
Figure 5.3: Illustration of the mo deled LC scenarios using the cell-grid metho d.
Dep end on the o ccupancy states of cell-grids, totally 8 scenarios (4 scenarios for eac h
LLC and RLC) can b e generated, as it is depicted in Figure 5.3:
•
LLC Sc enario 0_0 : When the ego v ehicle mak es LLC, there are no ob ject v ehicles
on b oth cell m and cell l .
•
LLC Sc enario 0_1 : When the ego v ehicle mak es LLC, there is no ob ject vehicle
on cell l but cell m is o ccupied.
•
LLC Sc enario 1_0 : When the ego v ehicle mak es LLC, there is no ob ject vehicle
on cell m but cell l is o ccupied.
•
LLC Sc enario 1_1 : When the ego v ehicle mak es LLC, b oth
cell m
and
cell l
are
o ccupied.
•
RLC Sc enario 0_0 : When the ego v ehicle mak es RLC, there are no ob ject v ehicles
on b oth cell m and cell r .
•
RLC Sc enario 0_1 : When the ego v ehicle mak es LLC, there is no ob ject v ehicle
on cell m but cell r is o ccupied.
•
RLC Sc enario 1_0 : When the ego v ehicle mak es LLC, there is no ob ject v ehicle
on cell r but cell m is o ccupied.
65

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
•
RLC Sc enario 1_1 : When the ego v ehicle mak es LLC, b oth
cell m
and
cell r
are
o ccupied.
The name of the LC scenarios termed Sc enario 0_1 and Sc enario 1_0 is in accordance
with the binary states of the o ccupancy cells illustrated in Figure 5.3.
5.4 Data pro cessing and feature extraction
5.4.1 Naturalistic driving data
Figure 5.4:
The exp erimen tal route of the SPMD pro ject. Picture extracted from Bezzina
and Sa yer (2014).
The naturalistic driving data that are used in this study is based on the pro ject of
Safet y Pilot Mo del Deplo ymen t (SPMD), whic h is a comprehensiv e data collection pro ject
in real-road condition. This third part y datasets is extremely useful for researc hers who
cannot conduct their o wn real-road exp erimen t. It is completely op en sourced whic h is
p opular in man y field of researc h.
The real-road pro ject includes m ulti-mo dal traffic, hosting appro ximately 3,000
v ehicles equipp ed with v ehicle-to-v ehicle (V2V) comm unication devices. The datasets
w e used w ere collected from 20 v ehicles, driving in the real-road including 75 miles of
roadw a y . The route is sho wn in Figure 5.4. Roads that mark ed as y ello w are the route
SPMD v ehicle dro v e. The driv ers v olun tarily joined in SPMD pro ject. They dro v e
the SPMD v ehicle completely based on their own driving st yles with no restriction
on their driving b eha viors. Eac h SPMD v ehicle w as equipp ed with data acquisition
66

5.4 Data pro cessing and feature extraction
systems (D AS) e.g. CAN and GPS as w ell as vision system lik e Mobiley e. All the
signals coming from differen t D AS w ere time-sync hronized with sampling rate at 10
Hz. Datasets are a v ailable on-line on the w ebsite of U.S. transp ortation departmen t
10
,
where DataF r ontT ar gets , DataL ane and DataWsu w ere do wnloaded for our study . The
description of the three datasets can b e found in Henclew o o d, Abramo vic h, and Y elc h uru
(2014) as follo ws:
•
DataF r ontT ar gets : Log of the data collected b y the Mobiley e sensor whic h is a part
of the D AS; largely includes data ab out the (v ehicle) ob ject that is in fron t of the
ego v ehicle.
•
DataL ane : Logs qualit y of the lane markings next to the ego v ehicle as w ell as the
distances b et w een eac h side of the v ehicle and eac h lane line.
• DataWsu : Log of GPS and CAN Bus data obtained via the on b oard W su.
The description of the tree t yp es of datasets regarding to the features of this study
is illustrated in T able 5.2, 5.3 and 5.4, resp ectively . The full details of the datasets can
b e found in Henclew o o d, Abramo vic h, and Y elc h uru (2014).
Device, T rip and Time are s ynchr oniz ed
Figure 5.5:
An example of the join ted datasets of DataF r ontT ar gets , DataL ane and
DataWsu using MySQL.
Due to the large quan tit y of the dataset (nearly 20 Gigabit), MySQL 6.3 is used to
query the datasets. In addition, although all the sensors installed in the SPMD v ehicles
w ere sync hronized, for researc h use all the three separated datasets, i.e. DataF r ontT ar gets ,
DataL ane and DataWsu , should b e join ted in one datasets. Figure 5.5 illustrates the
join ted datasets b y using MySQL, where w e can see that the whole datasets are
sync hronized b y device, trip and time.
10
h ttps://data.transp ortation.go v/A utomobiles/Safet y-Pilot-Mo del-Deploymen t-Data/a7qq-9vfe (vis-
ited on 31.07.2019)
67

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
T able 5.2: The description of datasets DataF r ontT ar gets
Data elemen t Units Description
Device none
A unique n umeric ID assigned to each D AS. This ID also
doubles as a v ehicle’s ID.
Time 1
10 s
Time in cen tiseconds since D AS started, whic h (generally)
starts when the ignition is in the on p osition.
T rip none
Coun t of ignition cycles. Eac h ignition cycle commences
when the ignition is in the on p osition and ends when it is
in the off p osition.
T argetId none
Numeric ID assigned b y the Mobiley e sensor to distinguish
b et w een the differen t ob jects b eing trac ked; the closest
obstacle is giv en a T argetId v alue of 1.
ObstacleId none
ID of new obstacle, as assigned b y the Mobiley e sensor, and
its v alue will be the last used free ID.
Range m
Longitudinal p osition of an ob ject, t ypically the closest ob ject,
relativ e to a reference p oin t on the ego vehicle, according to
the Mobiley e sensor.
RangeRate m/s
Longitudinal v elo cit y of an ob ject, t ypically the closest ob ject,
relativ e to the ego v ehicle, according to the Mobiley e sensor.
T ransv ersal m
The lateral p osition of the obstacle, as determined b y the
Mobiley e sensor.
Status none
Classification of the motion (kinematic state) of an iden tified
obstacle/target as stopp ed, mo ving, etc.
CIPV none
Field comm unicating whether an obstacle is the closest in a
v ehicle’s path.
T able 5.3: The description of datasets DataL ane
Data elemen t Units Description
Device none
A unique n umeric ID assigned to each D AS. This ID also
doubles as a v ehicle’s ID.
Time 1
10 s
Time in cen tiseconds since D AS started, whic h (generally)
starts when the ignition is in the on p osition.
T rip none
Coun t of ignition cycles. Eac h ignition cycle commences
when the ignition is in the on p osition and ends when it is
in the off p osition.
LaneDistanceLeft m
Distance b et w een the left side of the vehicle and the left
b oundary of the tra v el lane.
LaneDistanceRigh t m Distance b et w een the righ t side of the v ehicle and the righ t
b oundary of the tra v el lane.
68

5.4 Data pro cessing and feature extraction
T able 5.4: The description of datasets DataWsu
Data elemen t Units Description
Device none
A unique n umeric ID assigned to each D AS. This ID also
doubles as a v ehicle’s ID.
Time 1
10 s
Time in cen tiseconds since D AS started, whic h (generally)
starts when the ignition is in the on p osition.
T rip none
Coun t of ignition cycles. Eac h ignition cycle commences
when the ignition is in the on p osition and ends when it is
in the off p osition.
AxW su m / s 2 Longitudinal acceleration from v ehicle CAN Bus.
Y a wRateW su deg / s 2 Y a w rate from v ehicle CAN Bus.
Sp eedW su km/h Sp eed from v ehicle CAN Bus.
The metho d of querying LC ev en ts from h uge time series data is used b y Zhao,
Guo, and Jia (2017). T otally , 1375 lane-c hange cases (761 LLC and 614 RLC) are
extracted for analysis. The statistics of the LC cases with resp ect to the corresp onding
LC scenarios in Figure 5.3 can b e seen in T able 5.5. W e can see that in LLC scenarios,
most of the cases to ok place in LLC Sc enario 0_0 (365 cases) and LLC Sc enario 0_1
(354 cases). And in RLC scenarios, the dominating cases are RLC Sc enario 0_0 (371
cases) and RLC Sc enario 1_0 (214 cases). This result implies that when the driv er
w an ts to execute left/righ t LC maneuv er, he/she tends to w ait un til the destination lane
b eing empt y ( cell l / cell r is uno ccupied).
T able 5.5: The otal amoun t of LC cases
LC t yp e Scenario Amoun t
LLC
0_0 365
0_1 354
1_0 15
1_1 27
RLC
0_0 371
0_1 10
1_0 214
1_1 16
5.4.2 F eature extraction
V ehicle dynamic feature
V ehicle dynamic feature refers to features that can describ e the dynamic motion of the
ego v ehicle. V ehicle y a w rate and lateral acceleration are widely regarded as strong
69

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
features of v ehicle lateral b eha vior. T ogether with longitudinal acceleration, the ab o v e
signals are necessary for recognizing, predicting as w ell as mo deling v ehicle lateral
b eha viors (Leonhardt and W anielik, 2017; Higgs and Abbas, 2015; Li, Li, et al., 2015;
Luo et al., 2016). In this study w e also c ho ose the follo wing features that can b e directly
collected from the on-b oard sensors:
• y aw R ate t : y a w rate of the ego v ehicle at time t .
• az t : longitudinal acceleration of the ego v ehicle at time t .
• ax t : lateral acceleration of the ego v ehicle at time t .
Com bined feature
Com bined feature is the feature com bines differen t t yp es of features. The common
com bined features are as follo ws.
Time to collision (TTC) is the time required for t wo v ehicles to collide if they
con tin ue at their presen t sp eeds on the same path. It is usually used to ev aluate the
collision risk (Kusano and Gabler, 2011). If the driv er follo ws a v ehicle with a small
TTC, he/she ma yb e execute a LC to o vertak e the slo w leading v ehicle. Th us the TTC
can b e regarded as a v aluable feature to recognize driv er LC b eha vior (Kasp er et al.,
2012). Time-to-lane crossing (TLC) represen ts the time a v ailable for a driv er un til the
momen t at whic h an y part of the v ehicle reac hes one of the lane b oundaries (Go dthelp,
Milgram, and Blaau w, 1984). It is an indicator to estimate if the ego v ehicle is going to
cross the lane. Based on J2944 (2013), TTC and TLC are giv en b y
• TTC with the ob ject v ehicle in fron t on the curren t lane ( TTC t ) at time t:
TTC t = − R
˙
R (5.12)
where
R
and
˙
R
(in Figure 5.2b) are the range and the range rate b et w een the
fron t edge of the ego v ehicle and rear edge of the closest ob ject v ehicle in the same
tra v eling path as the ego v ehicle, resp ectiv ely . Here, what needs to b e men tioned
is that TTC is only calculated for the LC case when
Cell m
= 1 , b ecause
Cell m
= 0
means there is no v ehicle in the cell.
• TLC at time t ( TLC t ):
TLC t = dx
v x , (5.13)
where
dx
is lateral distance b et w een the fron t wheel and the lane b oundary of the
ego v ehicle. v x is the lateral sp eed.
In case that
˙
R
and
v x
are equal to zero, equation
(5.12)
and
(5.13)
b ecome infinit y ,
w e use the in v erse of TTC − 1
t and TLC − 1
t instead.
70

5.4 Data pro cessing and feature extraction
Time-windo w feature
Data collected from the on-b oard sensors are in time series, so using a time-windo w (TW)
to extract features is effectiv e to capture the information during the past few seconds
and this information can b e used to recognize up coming ev en ts (Thissen et al., 2003;
Salfner and Malek, 2007). In the case of predicting driv er LC b eha vior, differen t lengths
of TW b et w een 1 second to 5 seconds are selected for feature extraction (Mandalia
and Salvucci, 2005). In order to capture the prop erties of time series data, statistical
v ariables (mean, standard deviation, maxim um, minim um and median) are calculated
within eac h TW (Li, Li, et al., 2015) as is describ ed in T able 5.6, i.e. feature n um b er
6–80. The n um b er of the top righ t corner of the feature is the length of TW. Th us,
‘5’ in
mean _ y aw 5
t
means c ho osing 5 seconds as TW and ‘4’ in
mean _ y aw 4
t
represen ts
4 seconds TW, see feature # 6 and # 7 as examples. Figure 5.6a demonstrates ho w
time-windo w features are extracted.
F requency-domain feature
F requency-domain features ha v e already b een widely used in anomaly detection area as
w ell as detecting driv er men tal states (Chen et al., 2015; Chandola, Banerjee, and Kumar,
2009). F ast F ourier transform (FFT) is a p opular metho d to transform time-domain
signals in to frequency-domain (Hec kb ert, 1995). After FFT, the maxim um v alue of
FFT co efficien ts within TW is a go o d indicator to represen t the prop ert y of frequency
signals (Mörc hen, 2003). The description of the frequency-domain features are listed in
T able 5.6, i.e. feature n um b er 81–95. Figure 5.6b depicts ho w frequency domain features
are extracted.
𝑡 𝑡 𝑡 − 5 𝑠 𝑡 − 5 𝑠
(a) T ime-window feature extracion (b) Freq uency-dom ain f eature ex tracion
Extracting feature ,
i.e. max , min, std , med and m ean,
at mom ent 𝑡 with time-w indow 5 s.
Extracting feature after FFT ,
i.e. max , min, std , med and mean ,
at mom ent 𝑡 with time-windo w 5 s.
Fea tur e value
Fea tur e value

Figure 5.6: Illustration of the time-windo w feature and the frequency domain feature.
71

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
T able 5.6: Description of the extracted features.
# F eature name F eature description
1 y aw Rate t ya w rate of ego v ehicle at time t
2 az t az of ego v ehicle at time t
3 ax t ax of ego v ehicle at time t
4 TTC − 1
t TTC − 1
t at time t
5 TLC − 1
t TLC − 1
t at time t
6 mean _ y aw 5
t mean of y aw Rate in TW 5 s
7 mean _ y aw 4
t mean of y aw Rate in TW 4 s
8-10 .
.
. mean _ y aw t in TW 3 s , 2 s , 1 s
11 std _ y aw 5
t std of y aw Rate in TW 5 s
12-15 .
.
. std _ y aw t in TW 4 s , 3 s , 2 s , 1 s
16 max _ y aw 5
t maxim um of y aw R ate in TW 5 s
17-20 .
.
. max _ y aw t in TW 4 s , 3 s , 2 s , 1 s
21 min _ y aw 5
t minim um of y aw R ate in TW 5 s
22-25 .
.
. min _ y aw t in TW 4 s , 3 s , 2 s , 1 s
26 med _ y aw 5
t median of y aw Rate in TW 5 s
27-30 .
.
. med _ y aw t in TW 4 s , 3 s , 2 s , 1 s
31 mean _ az 5
t mean of the az in TW 5 s
32-35 .
.
. mean _ az t in TW 4 s , 3 s , 2 s , 1 s
36 std _ az 5
t standard deviation of az in TW 5 s
37-40 .
.
. std _ az t in TW 4 s , 3 s , 2 s , 1 s
41 max _ az 5
t maxim um of az in TW 5 s
42-45 .
.
. max _ az t in TW 4 s , 3 s , 2 s , 1 s
46 min _ az 5
t minim um of az in TW 5 s
47-50 .
.
. min _ az t in TW 4 s , 3 s , 2 s , 1 s
51 med _ az 5
t median of az in TW 5 s
52-55 .
.
. med _ az t in TW 4 s , 3 s , 2 s , 1 s
56 mean _ ax 5
t mean of the ax in TW 5 s
57-60 .
.
. mean _ ax t in TW 4 s , 3 s , 2 s , 1 s
61 std _ ax 5
t standard deviation of ax in TW 5 s
62-65 .
.
. std _ ax t in TW 4 s , 3 s , 2 s , 1 s
66 max _ ax 5
t maxim um of ax in TW 5 s
67-70 .
.
. mean _ ax t in TW 4 s , 3 s , 2 s , 1 s
71 min _ ax 5
t minim um of ax in TW 5 s
72-75 .
.
. min _ ax t in TW 4 s , 3 s , 2 s , 1 s
76 med _ ax 5
t median of ax in TW 5 s
77-80 .
.
. med _ ax t in TW 4 s , 3 s , 2 s , 1 s
81 max _ F _ y aw 5
t max y aw Rate FFT co efficien ts in TW 5 s
82-85 .
.
. max _ F _ y aw t in TW 4 s , 3 s , 2 s , 1 s
86 max _ F _ az 5
t max az FFT co efficien ts in TW 5 s
87-90 .
.
. max _ F _ az t in TW 4 s , 3 s , 2 s , 1 s
91 max _ F _ ax 5
t max ax FFT co efficien ts in TW 5 s
92-95 .
.
. max _ F _ ax t in TW 4 s , 3 s , 2 s , 1 s
72

5.5 Ev aluation metho d
5.4.3 Data lab eling
Ego
vehicle
1 0𝑠 5𝑠 𝑡 0
LK data LC data
1 5𝑠 Object
vehicle

Figure 5.7: Lab eling for LC and LK datasets.
In order to ev aluate the extracted features, LC datasets and LK datasets should b e
lab eled. T o lab el LC ev en ts, one of the most imp ortant things is to find
t 0
. T ak e LLC
for example. As it is sho wn in Figure 5.7, the ego v ehicle (blue) in tends to o v ertak e the
slo w v ehicle (red) b y left lane change. The momen t that the left wheel of the ego car
just crosses the cen tral dotted line is mark ed as the initial LC time
t 0
. Since there is
no ey e-trac k er equipp ed in SPMD v ehicles, the GBL metho d prop osed in section 4.4.1
cannot b e implemen ted. Instead, w e can only use the TWL metho d to lab el datasets.
Based on the study in Salvucci and Liu (2002), it suggests that the driv er tends to start
a LC maneuv er appro ximately 5 s b efore an accrual LC. Th us in this study , time series
data b et w een
t 0
and 5 seconds b efore are lab eled as LC data samples. T o ensure LK
datasets are separation of LC datasets, LK data samples are lab eled b et w een 10 seconds
and 15 seconds prior to t 0 . The same rule is applied to RLC.
5.5 Ev aluation metho d
5.5.1 F eature ev aluation
F rom the p ersp ectiv e of statistics,
p
-v alue is commonly used to test whether there is
statistical significance b et w een t w o groups. If there is statistical significance b et w een LC
datasets and LK datasets, it is th us to sa y that the extracted features are probably go o d
indicators for classification of LC and LK data samples. Ho w ev er, only using
p
-v alue to
ev aluate significance is not enough (Sulliv an and F einn, 2012). The effect size, suc h as
Cohen’s d (Cohen, 1988), is also an imp ortan t ev aluation metric (Cohen, 1990):
d = | M 1 − M 2 |
√ S 2
1 + S 2
2
2
(5.14)
where
d = Cohen’s index,
M 1 = mean of the first group data,
M 2 = mean of the second group data,
73

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
S 1 = standard deviation of the first group data,
S 2 = standard deviation of the second group data.
In order to define significance lev el, Cohen defines the effect class as follo w (J.Cohen,
1992):
• d < 0 . 5 : small effect
• 0 . 5 ≤ d < 0 . 8 : medium effect
• d ≥ 0 . 8 : large effect
F or eac h LC b eha vior, w e lab el LC and LK datasets and calculate b oth Cohen’s
d
and
p
-v alue for eac h feature. Then for all LC cases, w e a v erage the Cohen’s
d
and
p -v alues to get the mean for eac h feature in eac h scenario.
5.5.2 Mo dels used for feature ev aluation
In order to test if using the selected feature has adv an tages o v er using all the features
for ML mo dels, SVM, naiv e Ba y es (NB), Decision T ree (DT) and k-nearest neigh b ors
(KNN) are c hosen to ev aluate the classification p erformance. W e implemen t the ab o v e
ML mo dels through the Statistics and Mac hine Learning T o olb o x
11
pro vided b y Matlab.
Here, the SVM mo del is set with a Gaussian k ernel function, and the NB with Kernel
smo othing densit y estimation metho d, the DT with the default setting and the KNN
using an empirical prior with
k
= 1 . The datasets used for training those mo dels are
the same.
11 h ttps://www.math w orks.com/pro ducts/statistics.h tml (visited on 31.07.2019)
74

5.6 Result and analysis
5.6 Result and analysis
5.6.1 Analysis on effect size and p -v alue
All the ev aluation results (Cohen’s
d
and
p
-v alue) for eac h feature in b oth LLC and RLC
scenarios can b e found in App endix T able B.1 and B.2, resp ectiv ely . In statistical analysis,
p
-v alue that is smaller than 0.05 can b e regarded as ha ving statistical significance and
Cohen’s
d
that is greater than 0.8 represen ts a large effect lev el (J.Cohen, 1992). By
follo wing this t w o criterion, w e mark eac h feature with Cohen’s
d
greater than 0.8 and
p
-v alue smaller than 0.05 as red in T able B.1. So, those mark ed features are regarded as
strong features statistically and th us can b e used for classification of LC and LK data
samples. Ov erall, based on the features marked as red, w e find the follo wing results:
•
Although some features (
p <
0
.
05 ) ha v e sho wn statistical significance, marked as
blue, they ha v e only medium or small effect size lev el (Cohen’s
d <
0
.
8 ). This
result is also coincide with what it is refereed in Sulliv an and F einn (2012) that
only using p -v alue to ev aluate statistical significance is not enough.
•
V ehicle dynamic features e.g.
y aw R ate t
(#1),
az t
(#2) and
ax t
(#3), and com bined
feature
TLC − 1
t
(#5) are not strong features for LLC case with no items mark ed as
red. F or RLC case, only
az t
and
TLC − 1
t
in RLC Sc enario 0_1 can b e regarded as
strong features. This implies that from the statistical view the common empirical
kno wledge of using these features is not that m uc h con vincing. A dditional feature
selection w ork should b e done b efore using them.
•
W e men tioned that
TTC − 1
t
is only calculated when the fron t cell of the ego v ehicle
is o ccupied b y an ob ject v ehicle (
cell m = 1
).
TTC − 1
t
is mark ed as a strong feature
in LLC case, whic h demonstrates that the p oten tial of the rear-end collision do es
impact on driv er LC decision. A h yp othesis has b een made b y many resea rc h,
i.e. if the driver fol lows a le ading vehicle which is to o slow, he/she would pr ob ably
maneuver a LC to overtake the slow le ading vehicle . And this analysis from the
naturalistic driving data pro v es that this h yp othesis is reasonable.
• F eature #56 – #60, whic h refer to mean _ ax , represen t no statistical significance
at all and th us are regarded as unimp ortan t features.
•
T o analyze the time-windo w (TW) features (#6 – #95), w e tak e an example of the
mark ed strong features in LLC Sc enario 0_0 and LLC Sc enario 0_1 . T o mak e it
clear, w e segmen t the table horizon tally with 5 features in a group, e.g. feature #6
– #10 are related to the same feature
mean _ y aw
but with differen t TW from 5 s –
1 s etc. The detailed illustration in b oth LLC and RLC scenarios can b e seen in
T able B.1 and B.2 in App endix, where features that ha v e the largest Cohen’s
d
and
the smallest
p
-v alue are mark ed with ‘
▲
’ and ‘
▼
’, resp ectiv ely . F rom these p eak
and v alley v alues w e find that features with the greatest Cohen’s
d
are also lik ely
75

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
to ha v e the smallest
p
-v alues, except for feature #31 and #32 in LLC Sc enario
0_0 . W e select the final features for eac h scenario based on the mark ed p eak and
v alley features, and for the sp ecial case lik e feature #31 and #32, features with
greater Cohen’s d (e.g. #31) are selected.
5.6.2 Final selected features for eac h LC scenario
Based on the mark ed features from the results, the final selected features in b oth LLC
and RLC scenarios are listed in T able 5.7 and T able 5.8. I t can b e found that differen t
LC scenarios ha v e differen t features sets. The n um b er of selected features from all 95
features for eac h LC scenario ranges from 8 to 16 differen tly . There is no feature set
whic h is eligible for all the LC scenarios. This is wh y w e select the features based on
differen t LC scenarios. In LLC Sc enario 0_0 and LLC Sc enario 1_0 , RLC Sc enario
0_0 , and RLC Sc enario 1_0 , there are no v ehicle dynamic features and com bined
features (#2, #4, #5) selected whic h means that these features are ov erestimated.
Although v ehicle dynamic features related to v ehicle lateral mo v emen t (
y aw R ate t
(#1),
az t
(#2), and
ax t
(#3)) are not con tributiv e as exp ected, their corresp onding
time-windo w features indicate larger effect sizes. This implies that the prop ert y of the
v ehicle dynamic features within certain TW ma y con tain more imp ortan t information.
In addition, frequency-domain features are also promising features, with nearly at
least one feature b eing selected as strong feature in eac h scenario. The exception is
in LLC Sc enario 0_1 , where no frequency-domain features are selected. W e will use
the final selected features to train ML mo dels and further ev aluate their classification
p erformance.
76

5.6 Result and analysis
T able 5.7: The final selected strong features of eac h LLC scenario.
# F eature
LLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
4 TTC − 1
t – – 0.92 0.04 – – 1.18 0.01
6 mean _ y aw 5
t 1.02 0.03 – – – – 1.54 0.04
7 mean _ y aw 4
t – – 0.98 0.04 1.14 < 0 . 01 – –
11 std _ y aw 5
t – – 1.05 0.03 – – – –
12 std _ y aw 4
t – – – – – – 1.03 0.01
13 std _ y aw 3
t 0.99 0.04 – – – – – –
16 max _ y aw 5
t 1.00 0.04 – – – – – –
17 max _ y aw 4
t – – 0.97 0.03 – – 1.36 0.03
21 min _ y aw 5
t – – 0.95 0.03 – – – –
22 min _ y aw 4
t 1.03 0.03 – – – – 1.20 0.04
23 min _ y aw 3
t – – – – 1.04 0.03 – –
26 med _ y aw 5
t 0.92 0.04 – – – – – –
27 med _ y aw 4
t – – – – 0.95 < 0 . 01 – –
31 mean _ az 5
t 0.92 0.04 – – 0.87 0.01 – –
32 mean _ az 4
t – – 0.95 0.04 – – – –
36 std _ az 5
t 0.91 0.04 1.05 0.04 – – – –
38 std _ az 3
t – – – – 1.23 < 0 . 01 – –
41 max _ az 5
t 0.98 0.04 – – – – – –
42 max _ az 4
t – – 1.01 0.03 – – – –
43 max _ az 3
t – – – – – – 0.85 0.04
46 min _ az 5
t 0.93 0.03 0.90 0.04 – – – –
50 min _ az 1
t – – – – – – 1.04 0.03
51 med _ az 5
t 0.88 0.04 0.94 0.04 0.97 0.01 – –
54 med _ az 2
t – – – – – – 0.91 0.03
61 std _ ax 5
t – – – – 1.21 < 0 . 01 – –
62 std _ ax 4
t – – – – – – 1.02 0.03
68 max _ ax 3
t – – – – 1.06 0.04 – –
71 min _ ax 5
t 0.94 0.04 – – – – – –
72 min _ ax 4
t – – – – 0.99 < 0 . 01 – –
82 max _ F _ y aw 4
t – – – – 0.87 0.01 – –
83 max _ F _ y aw 3
t – – – – – – 1.14 0.04
86 max _ F _ az 5
t 0.98 0.04 – – – – 0.83 0.02
93 max _ F _ ax 3
t – – – – 1.18 < 0 . 01 – –
Selected amoun t 12 10 11 11
77

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
T able 5.8: The final selected strong features of eac h RLC scenario.
# F eature
RLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
2 az t – – 0.96 0.04 – – – –
4 TTC − 1
t – – – – – – 1.18 < 0 . 01
5 TLC − 1
t – – 0.82 0.02 – – – –
6 mean _ y aw 5
t 0.92 0.04 1.29 0.04 0.96 0.04 1.54 < 0 . 01
11 std _ y aw 5
t 1.01 0.03 – – 1.02 0.03 0.98 0.03
13 std _ y aw 3
t – – 1.60 < 0 . 01 – – – –
16 max _ y aw 5
t 0.97 0.03 – – 0.95 0.04 – –
18 max _ y aw 3
t – – – – – – 1.40 < 0 . 01
21 min _ y aw 5
t 0.98 0.04 1.10 0.02 1.06 0.02 1.38 < 0 . 01
26 med _ y aw 5
t – – – – 1.04 0.03 – –
28 med _ y aw 3
t – – – – – – 1.23 0.01
31 mean _ az 5
t – – 1.16 < 0 . 01 – – – –
32 mean _ az 4
t – – – – 0.98 0.04 – –
36 std _ az 5
t 0.91 0.04 0.85 0.02 0.94 0.04 – –
38 std _ az 3
t – – – – – – 0.92 0.03
41 max _ az 5
t – – – – 0.98 0.04 – –
42 max _ az 4
t 0.95 0.04 – – – – – –
46 min _ az 5
t 0.84 0.04 1.28 0.02 0.94 0.03 – –
51 med _ az 5
t – – 1.01 < 0 . 01 – – 0.94 < 0 . 01
52 med _ az 4
t – – – – 1.00 0.04 – –
61 std _ ax 5
t – – 1.16 0.02 – – – –
62 std _ ax 4
t – – – – – – 1.02 0.01
66 max _ ax 5
t – – 1.13 0.01 – – – –
71 min _ ax 5
t – – 1.10 0.02 – – – –
72 min _ ax 4
t – – – – – – 1.12 0.03
76 med _ ax 5
t – – 1.06 0.04 – – – –
81 max _ F _ y aw 5
t – – 0.95 0.01 – – – –
82 max _ F _ y aw 4
t 0.88 0.04 – – – – 1.20 0.03
86 max _ F _ az 5
t – – 0.88 0.02 0.95 0.04 – –
87 max _ F _ az 4
t – – – – – – 0.86 0.04
91 max _ F _ ax 5
t – – – – – – 1.16 0.02
94 max _ F _ ax 2
t – – 1.11 < 0 . 01 – – – –
Selected amoun t 8 16 11 13
78

5.6 Result and analysis
5.6.3 Ev aluation of differen t mac hine learning mo dels using
the selected features
In order to test if using the selected features could really impro v e the mo del p erformance,
w e compare the classification results of the ML mo dels in section 5.5.2 b oth with the
selected features (termed as ‘Selected’) and all the features (termed as ‘All F eatures’).
T o guaran tee that the training data and testing data are disjoint, a cross-v alidation
(CV) metho d is used. The datasets are ev enly divided in to ten folds. Nine folds are
used to train the mo dels and the remaining is used to test the mo dels. R OC curv es
and A UC v alues are as the metrics to ev aluate the mo del p erformance as it is used in
c hapter 4. All the R OC curv es regarding to the classification p erformance are illustrated
in App endix, where Figure B.1–B.4 are for LLC case and Figure B.5–B.8 for RLC case.
The corresp onding A UC v alues are listed in T able 5.9 and 5.10.
In T able 5.9 and 5.10, comparison is made b et w een using all the features and the
selected features for eac h ML mo del in b oth LLC scenarios and RLC scenarios. Since
the aim is to c hec k if the mo del p erformance can b e impro v ed b y using the selected
features instead of using all the features, w e denote ‘
↓
’ as p erformance deterioration.
The impro v emen t in p ercen tage can b e also seen from the table. Summarily , w e found
the follo wing result:
•
Using the selected features, the p erformance of KNN can b e impro v ed in all the
scenarios. DT has the similar results. Using selected features, the p erformance of
DT can b e impro v ed significan tly from 0.82 to 0.98 (an increase of 19.5%), and only
it only sho ws tin y deterioration (1.0%) in LLC Sc enario 1_1 and RLC Sc enario
1_1 .
•
F or SVM, using the selected features can largely impro v e the classification
p erformance (p erformance increase b et w een 4.2% and 13.6%) compared with
using all the features, and only sho w declination in LLC Scenario 0_1.
•
NB represen ts differen t pictures. Using the selected features can hardly ha v e
an y impro v emen ts compared with using all the features. F or example there is no
impro v emen t in all LLC scenarios. It also happ ens to RLC Sc enario 0_1 and RLC
Sc enario 1_1 . The p o or p erformance of NB ma y b e caused b y the conditional
indep enden t assumption b et w een features. This is the biggest do wnside of NB.
79

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
T able 5.9:
The A UC v alues of the classification results b y different models using the
selected features and all features in LLC scenarios.
F eature t yp e LLC Scenario 0_0 LLC Scenario 0_1
SVM NB DT KNN SVM NB DT KNN
All features 0.90 0.85 0.82 0.96 0.92 0.85 0.94 0.99
Selected 0.99 0.81 ↓ 0.98 0.98 0.84 ↓ 0.78 ↓ 0.97 0.99
Impro vemen t (%) 10.0 -4.7 19.5 2.0 -8.6 -8.2 3.1 0
F eature t yp e LLC Scenario 1_0 LLC Scenario 1_1
SVM NB DT KNN SVM NB DT KNN
All features 0.88 0.94 0.99 0.98 0.93 0.97 0.99 0.98
Selected 1 0.93 ↓ 0.99 1 0.99 0.96 ↓ 0.98 ↓ 0.99
Impro vemen t (%) 13.6 -1.0 0 2.0 6.4 -1.0 -1.0 1.0
T able 5.10:
The A UC v alues of the classification results b y different models using the
selected features and all features in RLC scenarios.
F eature t yp e RLC Scenario 0_0 RLC Scenario 0_1
SVM NB DT KNN SVM NB DT KNN
All features 0.94 0.87 0.91 0.97 0.95 0.94 0.99 0.96
Selected 0.98 0.80 ↓ 0.98 0.99 0.99 0.95 0.98 ↓ 1
Impro vemen t (%) 4.2 -8.0 7.6 1.0 4.2 1.0 -1.0 4.1
F eature t yp e RLC Scenario 1_0 RLC Scenario 1_1
SVM NB DT KNN SVM NB DT KNN
All features 0.93 0.83 0.92 0.97 0.92 0.93 0.97 0.96
Selected 0.97 0.75 ↓ 0.98 0.99 0.99 0.98 0.98 0.98
Impro vemen t (%) 4.3 -9.6 6.5 2.0 7.6 5.3 1.0 2.0
80

5.7 Summary
5.7 Summary
In this c hapter, w e aim to prop ose a feature selection metho dology in the view of
statistics. Unlik e other feature selection tec hnologies whic h select features for sp ecific
algorithm, the metho d prop osed in this study is more general and can b e used for all
the algorithms. In order to mak e the statistical analysis con vincing, a big data analysis
based on naturalistic datasets is implemen ted.
T o enric h the feature sets, v ehicle dynamic features directly collected from the
on-b oard sensors, com bined features e.g. TTC and TLC, as w ell as time-windo w
features w ere extracted. In addition, features are not restricted to time-domain features,
frequency-domain features are also considered. T otally 95 features w ere extracted. In
order to comprehensiv ely ev aluate the features based on driving scenarios, a 3-cell grid
w as used to mo del the con textual traffic. Both effect size (Cohen’s
d
) and
p
-v alue w ere
calculated as the metrics of feature selection. Results sho w that frequency-domain
features, whic h are rarely used in driv er b ehavior related researc h, are also promising
features, with nearly at least one feature b eing selected as strong feature in eac h scenario.
F rom the final selected feature sets w e find that for different LC scenarios, the final
selected features are differen t. Thus, feature selection should b e based on driving
scenarios. In addition, features refer to v ehicle lateral mo v emen t (
az t
and
TLC − 1
t
) whic h
are commonly used for recognition of driv er LC b eha vior, do not represen t statistical
significance (only except for
TLC − 1
t
in RLC Sc enario 0_1 ). This coun ter-empirical
result mak es it more w orth while doing feature selection w ork rather than just selecting
features based on the empirical kno wledge.
Finally , w e compared the classification p erformance of using the final selected features
to that of using all the features in eac h LC scenario. The result sho ws that except for
the relativ ely p o or p erformance of naiv e Ba y es, the p erformance of SVM and Decision
T ree, as w ell as KNN, can b e impro v ed from differen t lev els in most of the LC scenarios.
Summarily , the high p erformance ac hiev ed b y the ML mo dels using all the features
(95 features) is at the exp ense of computation time. Considering the fact that using
the selected features (nearly only 10 features), ML mo dels can still ac hiev e the same
p erformance or ev en ha v e impro v emen t, it can b e concluded that it is more efficien t and
effectiv e to using the selected features.
Summarily , the metho dology presente d in this study can b e used for selecting an y
features regarding to driv er LC b eha vior, ho w ev er, there are still some limitations that
can b e impro v ed in the further study:
•
the ra w naturalistic datasets are pro vided b y the third part y from U.S, where the
driving scenarios and driving rules are sligh tly differen t from German y . In differen t
driving scenarios and based on differen t traffic rules, the final selected features as
w ell as feature ev aluation result ma y lead to differen t results.
81

5. Big data analysis - Ev aluation of feature selection for driv er lane-c hange
b eha vior
•
the extracted feature sets in this study (in T able 5.6) are limited b y v ehicle dynamic
features (lik e y a w rate, acceleration etc.). There are no features related to driv er
b eha vior. F or example, driver ey e mo v emen ts, driv er maneuv ering features e.g.
steering wheel angle and brak e p edal data etc. Since there are no suc h features
a v ailable in the naturalistic datasets w e used.
In order to o v ercome the limitations men tioned ab o v e and to further ev aluate the
framew ork prop osed in c hapter 4, an exp erimen t conducted on German road is necessary .
82

6
Exp eriment 2 - Evaluation based on a
real-road exp eriment
6.1 In tro duction
In order to further ev aluate the metho ds prop osed in the last t w o c hapters and to
o v ercome the limitations, a real-road based study is made in this c hapter. The con ten t
includes exp erimen tal design, data pro cessing, data lab eling, feature selection as w ell as
ev aluation. The real-road exp erimen t w as carried out in the p erio d b et w een 15.01.2019
to 19.03.2019 with 12 sub jects participated 12 .
6.2 Exp erimen tal design
This part details the whole design of the real-road exp erimen t whic h includes the
materials and equipmen t, and the sync hronization metho d for data acquisition as w ell
as participan ts recruiting and driving task description.
6.2.1 Equipmen t
T esting v ehicle
The testing v ehicle is BMW 520 T ouring with diesel engine and 8-gear automatic
transmission. There are some AD AS functionalities in the testing v ehicle, suc h as head-
up displa y , adaptiv e cruise con trol system, lane departure w arning system and parking
assistance system. Ho w ev er, except for head-up displa y , during the en tire exp erimen t
participan ts are not allo w ed to use the rest functionalities.
12
This exp erimen t is supp orted b y Jo yson Safet y Systems Gm bH. They pro vided the testing v ehicle
and the necessary sensors. In addition, the cost of recruiting participan ts is also co v ered b y the compan y .
83

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
On-b oard CAN bus systems
The signals from sensors w ere collected through CAN bus systems and w ere recorded b y
an on-b oard PC, whic h w as lo cated b elo w the truc k flo or of the v ehicle. T otally there
w ere four CAN bus systems used for data collection, i.e. S-CAN, K-CAN, PT1 and
PT2-CAN
13
. The lo cation of the four CAN systems and the on-b oard PC is sho wn in
Figure 6.1.
Figure 6.1:
CAN bus setup in the testing v ehicle. Picture extracted from Zühlsdorff
(2018), where the same testing v ehicle is used as in this study .
Figure 6.2:
The first p erson view from the participan t who w ears the SMI glasses is
driving on the high wa y , where the blue p oin t is the fixation monitored b y ey e-trac k er.
Ey e-trac k er setup
The ey e-trac k er used in this exp erimen t is the same as it is used in chapter 4, see
Figure 4.5. The difference is that in c hapter 4, the exp eriment w as conducted in
13 S-CAN, K-CAN, PT1 and PT2-CAN are differen t CAN c hannels.
84

6.2 Exp erimen tal design
a driving sim ulator but in this c hapter the exp erimen t w as a real-road exp erimen t.
Figure 6.2 illustrates the first p erson view from one participan t on high w a y . The en tire
driving scenario during the test in the view of the participan t can b e recorded b y the
ey e-trac king glasses, whic h can b e used for further data pro cessing. The sampling rate
of the ey e-trac k er is at 30 Hz.
Data sync hronization
Since the ey e-trac king data are not collected through CAN bus, a real-time sync hro-
nization b et w een signals from CAN bus and the ey e-trac k er is crucial for further data
pro cessing.
The real-time sync hronization w ork is done as follo ws: dur ing the exp eriment,
messages are sen t from PC used for recording CAN signals (Figure 6.1) to the ey e-
trac k er at a frequency of 0.1 Hz. In Figure 6.3a, the blue p oin ts are the messages receiv ed
b y the ey e-trac k er from the on-b oard PC. Eac h message con tains a Unix timestamp
in seconds based on the curren t time from PC and the ey e-trac ker timestamp. An
example of the messages for sync hronization can b e seen in Figure 6.3b, where the
term Timestamp is the ey e-trac k er time and V alue is the Unix timestamp
14
from the
on-b oard PC. In this w a y , data collected from the on-b oard PC and the ey e-trac k er is
sync hronized.
6.2.2 P articipan ts
Based on the regulation of Jo yson Safet y Systems Gm bH, only the emplo y ees of the
compan y could driv e the testing v ehicle, thus the participan ts w ere selected among
the p ersonnel. Since the participan ts w ere ask ed to w ear ey e-trac king glasses during
the en tire driv e, for calibration issue
15
, only participan ts who ha v e normal vision or
w ear con tact lenses can meet the requiremen t. The questionnaire that is used to select
participan ts can b e found in App endix C.1.1.
T otally 12 v olunteers (6 male and 6 female driv ers) participated the exp erimen t,
among whic h 9 participan ts are in the age group b et w een 25 and 39, 2 participan ts in the
age group b et w een 40 - 54 and one is o v er 55. All the participan ts are nativ e German,
ha ving had their driv er licenses with minim um 9 y ears and maxim um 45 y ears (Mean =
19.08 y ears). They are familiar with the traffic rules in German y , and could understand
the questionnaire and the instruction in German without ha ving an y trouble. The
detailed statistics of the demographic questionnaire regarding to participan ts’ gender,
age, driving kilometers as w ell as driving frequency can b e found in App endix Figure C.1.
14
The unix time stamp is a w a y to track time as a running total of seconds. This coun t starts at
the Unix Ep o ch on Jan uary 1st, 1970 at UTC. This is v ery useful to computer systems for trac king
and sorting dated information in dynamic and distributed applications b oth online and clien t side.
h ttps://www.unixtimestamp.com/ (visited on 31.07.2019)
15
If the driv er w ears glasses, the reflection of the glasses ma y lead to the failure of calibrating SMI
ey e-trac king glasses.
85

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
(a)
A screen shot tak en from the soft ware used to process eye-trac king data, where
the blue p oin ts are the messages from PC used to record CAN signals carrying Unix
timestamps.
(b) Illustration of sync hronized timestamps b et w een CAN and the eye-trac k er.
Figure 6.3: Data sync hronization b et w een CAN and the ey e-trac ker.
0 2 4 6 8 10 12 14 16 18
Scores
0
1
2
3
4
5

Figure 6.4: Illustration of the aggressiv eness scores of the participan ts in histogram.
86

6.2 Exp erimen tal design
High (score > 10)
Medium (5 <= score < 10)
Low (0 <= score < 5)

Figure 6.5:
The pie c hart of classification of driving st yles based on scores, where high,
medium and lo w represents the lev els of aggressiv e driving styles.
In order to classify the driving st yles of the participan ts, the same b eha vioral-
psyc hological questionnaire as it is describ ed in c hapter 4 (in App endix A.1.2) is used.
Figure 6.4 illustrates the aggressiv eness scores of all the participan ts. Based on the score
of classification from 32 participan ts in chapter 4.3.3, the driving st yle classification
results of the 12 participan ts in this exp erimen t is as follo ws and the distribution of the
pie c hart can b e found in Figure 6.5:
• Lo w aggressiv e driving st yle: scored b et w een 0 and 5.
• Medium aggressiv e driving st yle: scored greater than 5 and smaller than 10.
• High aggressiv e driving st yle: scored o v er 10.
The ab o v e classification of driving st yle groups are used for organizing the training
datasets whic h will b e detailed in section 6.3.
6.2.3 Driving task
Driving instructions
After signing a declaration of consen t to store p ersonal data and completing a
demographic questionnaire as w ell as the b eha vioral-psychological questionnaire, the
participan ts w ere instructed b y the assist
16
ab out ho w to use the testing v ehicle b efore
start the journey .
16
There w ere t w o assists who are t w o master studen ts sitting on bac k ro w of the testing vehicle. One
assist w as resp onsible for giving on-b oard instruction and c hec king the status of the on-b oard PC whic h
is used for recording data. Another assist w as resp onsible for man ually lab eling the driving b eha viors
of the participan ts whic h is detailed in 6.3.
87

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
The participan ts w ere informed that their ey e mo v emen ts will b e recorded b y the
ey e-trac k er, and also the GPS data, driving dynamics mo v emen ts. But the exact purp ose
of the exp erimen t w as not told to them in order not to impact on their driving habits.
The functionalities of some assistance systems whic h are a v ailable in the v ehicle w ere
explained, e.g. the adaptiv e cruise con trol and head up displa y , but the participan ts
can only use the head up displa y . The participan ts w ere also told the pro cedure of the
exp erimen t, e.g. ho w long it tak es, when to break for a while, the p ossible on-b oard
instruction, calibration of the ey e-trac k er as w ell as ho w to ab ort the exp erimen t if they
do not w an t to con tin ue etc. The whole instruction do cumen t can b e found in App endix
in C.1.4.
Calibration
A 3-p oin ts calibration metho d is used to calibrate the ey e-trac king glasses. As w e can
see in Figure 6.6, the participan t w as ask ed to fo cus his/her fixation on a 3-b y-3 matrix
card. The three orange squares are used for calibration.
Figure 6.6:
The 3-b y-3 matrix card used for calibration of the ey e-trac k er using, where
the red p oin t is the fixation p oin t of the participan t.
88

6.3 Data pro cessing
Route of the exp erimen t
After calibration, the participan ts can start their journey . They do not need to remem b er
the w a y or use na vigation; instead, all the driving guidance w as giv en b y the assist. The
route of the en tire exp erimen t is illustrated in Figure 6.7, whic h follo ws clo c kwise. The
start and end p oin t is Hussitenstr. 34, where Jo yson Safet y System Gm bH is lo cated.
There are three stages of driving and t w o breaks during the exp erimen t. The first and
the third are cit y scenarios, b et ween them is on high w a y A10 where some part of the
route has no sp eed limits. The net driving duration is around t w o hours and a half
excluding the t w o breaks. T w o breaks are b efore and after driving on high w a y so that
the participan ts w ould not b e fatigue b ecause of long time driving. During the break the
participan ts do not need to w ear the ey e-trac k er an ymore, but re-calibration is needed
b efore they start again.
Figure 6.7:
A screen shot of go ogle map whic h captures the route (in clo c kwise) of the
exp erimen t in Berlin.
6.3 Data pro cessing
After t w o mon ths, the whole exp erimen t w as finished with totally 158 Gigab yte data
collected. Data collected b y SMI ey e-trac ker are the ma jorit y , taking up 152 Gigab yte.
The videos of the en tire exp erimen t w ere also recorded as it is sho wn in Figure 6.2. Data
from CAN bus tak e up the rest 6 Gigab yte. All the data w ere stored separately b y eac h
participan t. After c hec king the in tegrit y of the datasets, it is found that data collected
from t w o participan ts failed to b e sync hronized and th us w ould not b e used for further
89

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
study . In addition, only data collected on high w a y (the second stage in Figure 6.7) is
pro cessed since the fo cus of this study is mainly on high w a y roads 17 .
6.3.1 Ey e-trac king data pro cessing
The soft w are used for pro cessing the ey e-trac king data in this exp erimen t is softw are
BeGaze 3.7, whic h is the up dated v ersion of BeGaze 3.6 used in c hapter 4 with no new
feature upgraded.
Defining A oI for real-road scenario
A ccording to Lee, Olsen, Wierwille, et al. (2004), the most lik ely glancing lo cations
while driving are forw ard, rear view mirror, left mirror as w ell as blind sp ot region
lik e left side windo w and righ t side windo w. In exp erimen t 1 presen ted b y c hapter
4, the setting of A oIs is limited b y the driving sim ulator. In the sim ulator scenario,
there is no ro om for the participan t to c hec k the blind sp ot. See Figure 4.4 where the
sim ulated driving scenario is pro jected on the w all with no blind sp ot. Limited b y
this exp erimen tal condition, only fiv e A oIs are defined, i.e. R e ar mirr or , L eft mirr or ,
R ight mirr or , Sp e e dometer and Wind scr e en . Considering blind sp ot c hec king is a v ery
imp ortan t driv er b eha vior b efore executing a LC maneuv er, conducting the exp erimen t
using the driving sim ulator suffers this limitation. Ho w ev er for the real-road exp erimen t,
there is no suc h limitation at all. T w o additional A oIs are defined in this study , i.e. L eft
window and R ight Window , used for monitoring driv er blind sp ot c hec king b eha vior.
Figure 6.8 is the illustration of the defined A oIs for the read-road driving scenario. The
orange and blue p oin ts are the fixations of the participan ts.
Although in the real-road scenario it is p ossible to define more flexible A oIs than in
the driving sim ulator scenario, it is also more c hallenging to lab el eac h defined A oI than
in the driving sim ulator scenario. The reason is that in the driving sim ulator exp erimen t,
due to the limited viewing angle (the participan ts is fo cusing on the pro jected scenario on
the w all), the participan ts do not need to c hec k the blind sp ot so their head orien tation
are nearly fixed. With nearly fixed head orien tation, the recorded frame b y the ey e-
trac k er is also nearly fixed
18
. This mak es it easier to lab el A oIs for eac h frame. Ho w ev er,
in the real-road scenario, the participan ts are more lik ely to rotate their heads in order
17
The target of this study is on high wa y road, ho wev er, the exp erimen t w as conducted b oth on
high w a y and in cit y road. The reason is that this real-road exp eriment, whic h is supp orted by Jo yson
Safet y Systems Gm bH, is not only for the purp ose of this study but also for other researc h purp oses
b y the compan y itself. This w ould not impact on this study since all the participants w ere not told
the purp ose of the exp eriment, but w ere told to driv e lik e normal without giving them any additional
instruction.
18
Fixations, whic h are presen ted b y the orange and blue p oin ts in Figure 6.8 are frame based. That
is to sa y , the co ordinate of the fixation is actually the pixel co ordinate in reference image. Even for
the same A oI, the fixation co ordinate in differen t recorded images can b e differen t. F or example, the
fixation co ordinates in Figure 6.8a and in Figure 6.8d are lik ely the same, how ev er, they represen t
differen t AoIs. The former refers to L eft window but the latter refers to L eft mirr or . The same situation
happ ens to Figure 6.8c and Figure 6.8f.
90

6.3 Data pro cessing
(a) Left window
(b) Rear mirror
(c) Right window
(d) Left mirror
(e) Speedometer
(f) Right mirror
(g) Wind screen

Figure 6.8: The definition of the 7-region A oIs in soft w are BeGaze 3.7.
to c hec k the driving situations. The result is that the recorded frame is also c hanging
frequen tly , whic h mak es it difficult to use the co ordinate v alue of the fixation to lab el
A oIs. Th us, lab eling A oIs for the read-road data is more c hallenging than the driving
sim ulator data. A Seman tic gaze mapping metho d is used to lab el A oIs.
Seman tic gaze mapping
Fixations and saccades are t w o imp ortan t gaze ev en ts whic h indicate human’s certain
in ten tion (Liu, Yttri, and Sn yder, 2010). By monitoring fixation and saccade, Maltz and
Shinar (1999) found that y ounger driv ers and older driv ers represen t significan t differen t
visual information pro cessing pattern. Some researc hes wer e fo cusing on iden tifying
fixation and saccade (Salvucci and Goldb erg, 2000; Nyström and Holmqvist, 2010). In
this c hapter w e use a seman tic gaze mapping w a y to lab el A oIs.
Seman tic gaze mapping is to iden tify all the fixation and saccade ev en ts, and order
them c h unk b y c h unk in time series. The fixations and saccades are pro cessed b y
SMI BeGaze 3.7. In Figure 6.9, the green c h unks are fixations and the length of the
c h unk represen ts the duration of the fixation. Bet w een t w o adjacen t fixation c h unks are
91

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
saccades. A oIs defined in Figure 6.8 are th us lab eled b y the seman tic c h unks. In order
to a v oid missing lab els, w e man ually lab eled all the A oIs, c h unk b y c h unk 19 .
After lab eling all the A oIs, the en tire gaze pattern including fixations and saccades as
w ell as the A oIs can b e represen ted in a reference frame, see Figure 6.10. This example
uses the data collected from the real-road exp erimen t b y one of the participan ts. In
Figure 6.10a, the p oin ts in circle are the fixations. The circle with a larger radius means
a longer duration. Bet w een t w o fixations is the scan path of corresp onding saccade.
W e can clearly see that the fixations fall in to our pre-defined A oIs. The distribution
of the fixations as a heat map can b e seen in Figure 6.10b. Regions that co v ered with
larger areas with red color mean that there are more fixations falling in. So Wind scr e en
and L eft mirr or are the most p opular A oIs for this participan t. The data format as
w ell as the con ten t of the output data after lab eling A oIs can b e found in App endix in
Figure C.2.
Fixation Saccade

Figure 6.9: A screen shot represen ts the seman tic gaze ev en ts in softw are BeGaze 3.7.
6.3.2 P arsing CAN data
In section 6.2.1 w e men tioned that all the signals from on-b oard sensors w ere collected
through CAN bus and recorded b y an on-b oard PC. T o use the ra w data for further
study , parsing CAN data is needed
20
. The parsed CAN signals are listed in T able 6.1
whic h illustrates the name and origin of the signals.
19
Man ually lab eling A oIs is time consuming, ho wev er, to get high qualit y A oI lab els it is w orth the
effort. This w ork was done b y the author of this dissertation and a master studen t. F or eac h participan t,
it tak es around 2 hours. If there are considerable more data need to b e lab eled, it is b etter to use
computer vision tec hniques.
20
Thanks to Jo yson Safet y Systems GmbH who pro vides all the necessary in terpretation ab out the
DBC file for the testing v ehicle BMW 520, so that the CAN data can b e parsed.
92

6.3 Data pro cessing
(a)
Scan path of gaze mo vemen t as w ell as the fixation pattern
with resp ect to the defined A oIs.
(b)
Heat map of gaze fixation pattern with resp ect to defined
A oIs.
Figure 6.10:
A reference frame whic h illustrates the gaze map hitting on AoIs, where
the term L Window refers to L eft window and R Window refers to R ight window .
93

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
T able 6.1: Illustration of the parsed CAN signals.
Name of signal Origin of signal T yp e of CAN
SF_CAN_D V_ACTN_STW Steering wheel S-CAN
SF_CAN_D V_ACTN_BR TOR Q Brak e p edal S-CAN
SF_CAN_D V_ACTN_A CPD Throttle sensor S-CAN
SF_CAN_V_VEH Sp eed sensor S-CAN
SF_CAN_VY A W_VEH IMU S-CAN
NA V_GPS1 GPS S-CAN
BLINKEN T urn signal S-CAN
OBJDT_HD WOBS A CC S-CAN
Since the sampling rate b et w een eac h CAN signal is unequal, a linear in terp olation
metho d is used to equalize the n um b er of the samples among differen t signals. An
example of linear in terp olation is sho wn in Figure 6.11, where at
t 2
,
t 4
,
t 6
and
t 8
there
are no data collected, th us a linear in terp olation metho d can mak e up for the missing
data. After re-sampling, all the data from CAN bus are at 25Hz. F or data fusion
purp ose, ey e-trac king data, whic h w ere collected at 30Hz, are do wn-sampled at 25Hz to
matc h the CAN bus data. In this w a y , all the data are at 25Hz sampling rate.
Raw dat a
Int erpolat ed
t
v alue
𝑡 1 𝑡 2 𝑡 4 𝑡 6 𝑡 8
𝑡 3 𝑡 5 𝑡 7 𝑡 9
Figure 6.11: An example of ho w linear in terp olation w orks.
94

6.3 Data pro cessing
6.3.3 F eature extraction
Although the feature extraction metho d as w ell as the extracted feature sets is already
illustrated in c hapter 4, how ev er, due to the difference of the sensors, the datasets of the
former study are differen t from the datasets in this c hapter. In c hapter 4, the testing
v ehicle w as equipp ed with Mobiley e system, whic h can detect targets in fron t of the ego
v ehicle on differen t lanes. In addition, the lane mark can also b e detected b y the vision
system. Ho w ev er, the testing v ehicle in this study is limited b y no suc h sensors, only
the forw ard targets driving on the same lane with the ego vehicle can b e detected.
The adv an tage of the data collected in this study o v er the datasets from the third
part y in the last c hapter is that there are more data t yp es included, e.g. steering wheel
angle, brak e and gas p edal data etc. And w e can also equip ey e-trac k er in the exp erimen t.
Th us the feature extraction w ork should b e reconsidered.
V ehicle dynamic feature
V ehicle dynamic feature refers to the feature that can describ e the dynamic motion of
the ego v ehicle. The extracted features are:
• y aw R ate t : y a w rate of the ego v ehicle at time t .
• a t : absolute acceleration of the ego v ehicle at time t .
Driv er b eha vior feature
F eatures whic h reflect driv er b ehavior are extracted as follo ws:
• br pd t : brake p edal pressure of the ego v ehicle at time t .
• acpd t : throttle op ening angle of the ego v ehicle at time t .
• stw t : steer wheel angle of the ego v ehicle at time t .
• stw R ate t : steering wheel angle rate of the ego v ehicle at time t .
Com bined feature
In section 5.4.2, t w o com bined features, i.e. time to collision (TTC) and time-to-lane
(TLC) crossing are considered. But the testing v ehicle in this exp erimen t cannot detect
the lane mark, TLC is not p ossibly calculated. Only TTC is considered as the com bined
feature. The in v erse is giv en b y 21 :
TTC − 1
t = v eg o
∆ d (6.1)
21
As it is men tioned in section 5.4.2, if the v elo cit y of the ego v ehicle and the fron t vehicle is the
same, TTC b ecomes infinite.
95

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
where
v eg o
and ∆
d
is the v elo cit y of the ego v ehicle and the distance with the fron t
v ehicle at time
t
, resp ectiv ely . The motion of the ego v ehicle relativ e to the fron t v ehicle
is illustrated in Figure 6.12.
𝞓𝑑
Ego
vehicle
Front
vehicle
𝑣 𝑒𝑔𝑜

Figure 6.12: Illustration of the motion of the ego v ehicle.
Time-windo w feature
The w a y of extracting time-windo w (TW) features is the same as it is describ ed in
section 5.4.2. TW b etw een 1 second to 5 seconds are used. Statistical prop erties lik e
mean, standard deviation, maxim um, minim um and median of the features men tioned
ab o v e are considered.
F requency-domain features
F ast F ourier transform (FFT), whic h is already describ ed in section 5.4.2, is used to
transform the time-domain features in to frequency-domain (Hec kb ert, 1995). After FFT,
the maxim um v alue of FFT co efficien t within TW is c hosen as the feature v alue (Mörc hen,
2003). All the extracted features in this exp erimen t can b e found in App endix T able C.1.
6.4 Metho d
6.4.1 Lab eling lane-c hange dataset
It is kno wn that the classification p erformance of the sup ervised learning mo dels is
highly dep ended on class lab els. In section 4.4, t w o LC lab eling metho ds are prop osed,
i.e. a gaze-based lab eling (GBL) and a time-windo w lab eling (TWL) metho d. F or the
real-road exp erimen t, w e also use these t w o lab eling metho ds to lab el LC data samples.
On-b oard lab eling lane-c hange ev en t
In order to impro v e the efficiency of the off-line data lab eling task, i.e. quic kly querying
LC ev en ts from h uge collected raw data, an on-b oard LC ev en t lab eling metho d w as
used. In this metho d, an assist sitting on bac k of the testing v ehicle w as resp onsible for
96

6.4 Metho d
Figure 6.13:
The k eyb oard whic h is used for manually labeling lane-change ev en ts
on-b oard.
lab eling LC ev en ts in real-time using a k eyb oard in Figure 6.13. When the participan t
prepared to mak e LC, he/she w ould p erform certain pre-LC b eha viors, e.g. mirror
glancing, blind p oin t c hec king, and then execute the actual maneuv er. The task of the
assist w as to notice suc h b eha viors and lab el the start time (the participan t steers the
steering wheel) b y pr essing and end time (LC finish) b y r ele asing k ey 1
22
. This lab eling
pro cess can b e illustrated in Figure 6.14, where the assist presses k ey 1 from
t star t
to
t end
to mark this lane-c hange ev en t. This on-b oard lab eling metho d is v ery useful and
efficien t to query eac h LC ev en t from the big time series data.
t end
functions as a v ery
imp ortan t criterion to extract LC and LK datasets, whic h will b e detailed in the data
lab eling section.
Front ve hicle
Ego vehicl e
𝑡 𝑠𝑡𝑎𝑟𝑡 𝑡 𝑒𝑛𝑑 𝑡 0
Steering wheel turning direction

Figure 6.14: Illustration of the selected time of the on-b oard lab eling task.
22
Note that the k eyb oard w as not only used for lab eling driv er LC b eha vior, but also for curve
driving, turning at the in tersection, for cit y scenarios etc. These b eha viors are not the researc h scop e
of this study .
97

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
T able 6.2 lists the n um b er of LC maneuv ered b y eac h participan t. F or the case that
the participan t attempted to mak e a LC but due to certain reasons, e.g. danger, he/she
ab orted the LC maneuv er, it is coun ted as A b orte d LC . T otally there are 232 LLC and
227 RLC cases observ ed. The LC cases that the driv er did not use the turn signal 8
seconds b efore executing a LC are also mark ed. It indicates that this situation happ ened
more in RLC case than LLC case. In addition, the classification of driving st yles of eac h
participan t from section 6.2.2 is also listed in the right column.
T able 6.2: The statistics of LC cases in the real-road exp erimen t.
# P articipan t LLC RLC Ab orted LC Driving st yle
1 19 19 0 High aggressiv e
2 18 18 3 Medium aggressiv e
3 23 23 3 Lo w aggressive
4 27 26 4 High aggressiv e
5 23 22 2 High aggressiv e
6 30 29 0 Lo w aggressive
7 16 16 1 Medium aggressiv e
8 30 30 1 High aggressiv e
9 28 26 4 High aggressiv e
10 18 18 1 High aggressiv e
Sum 232 227 19 -
No turn signal 12 21 - -
Gaze-based lab eling metho d
The rule of the gaze-based lab eling (GBL) metho d is the same as it is in section 4.4.1,
where the momen t
t pr epar e
is the critical momen t. T ak e the LLC case for instance. As it
is demonstrated in Figure 6.15a,
t pr epar e
is defined b y the last mirror-glancing b eha vior
b y the participan t b efore he/she uses the turn signal to indicate a LC. If the turn signal
is observ ed b efore a mirror-glancing b eha vior, w e c ho ose the momen t he/she indicates
the turn signal as the
t pr epar e
. It is the same rule for RLC case as it is depicted in
Figure 6.15b. The LC case whic h will not b e used for data lab eling is when the driv er
do es not use the turn signal 8 seconds b efore
t 0
. T otally there are 12 cases and 21 cases
for LLC and RLC, resp ectiv ely , whic h is illustrated in T able 6.2.
The statistics of
t pr epar e
in b o x plot is plotted in Figure 6.16, where the y axis is the
duration b et w een
t pr epar e
and
t 0
in seconds. Then LC datasets can b e lab eled b et w een
t pr epar e
and
t 0
.
t 0
is defined as the momen t that the wheel of the ego v ehicle just crosses
the dotted cen tral line. In order to obtain a balanced dataset for training, lane-k eep
(LK) data samples are lab eled as the equal n um b er as the LC data samples.
98

6.4 Metho d
Ego vehicl e
𝑡 𝑝𝑟𝑒𝑝𝑎𝑟𝑒 𝑡 0
lane-keep
LK dataset LC dataset
glancing left view mirror

(a)
Gaze-based lab eling metho d for LLC case. The n um b er of lab eled LC samples
and LK samples is equal.
Ego ve hicle
𝑡 𝑝𝑟𝑒𝑝𝑎𝑟𝑒 𝑡 0
lane-keep
LK dataset LC dataset
glancing right view mirror

(b)
Gaze-based lab eling metho d for RLC case. The n um b er of lab eled LC samples
and LK samples is equal.
𝑡 0
LC dataset
𝑡 0 ′
T rajectory of the ego- vehicle
LK dataset
same length
with LC
datasets
𝑡 𝑝𝑟𝑒𝑝𝑎𝑟𝑒
𝑡 𝑒𝑛𝑑 ′

(c) The case whic h is not suitable of using GBL metho d.
Figure 6.15: Demonstration of using the GBL metho d to lab el LC and LK datasets.
99

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
t_prepare LLC
0
2
4
6
8
s
t_prepare RLC
2
4
6
8
s

Figure 6.16: The b o x plot of t pr epar e ahead of t 0 .
A sp ecial case w e cannot use the GBL metho d is pictured in Figure 6.15c. That is:
when
t 0 − t pr epar e > t pr epar e − t ′
end
, where
t ′
end
refers to the end of the last adjacen t LC
ev en t. In other w ords, the difference b et w een
t ′
end
and
t pr epar e
should b e greater than
t pr epar e
and
t 0
, otherwise it is not p ossible to extract equal amoun t of LK data samples
as LC datasets.
Time-windo w lab eling metho d
The time-windo w lab eling (TWL) metho d uses a fixed length time in terv al to lab el data
samples. As w e can see in Figure 6.17a, candidate TW, i.e. 5s, 4s, 3s, 2s, 1s are selected.
Th us data b efore
t 0
and within the selected TW are lab eled as LC dataset. Righ t b efore
LC dataset, the same amoun t of data are then lab eled as LK samples for the need of
obtaining balanced datasets. This rule applies to b oth LLC and RLC case.
The criterion of c ho osing the largest TW for certain LC case follo ws what it is
illustrated in Figure 6.17b:
TW ⩽ t 0 − t ′
end
2 (6.2)
where
t 0
refers to the curren t LC ev en t, and
t ′
end
refers to the end of the last adjacen t
LC ev en t. The reason of restricting the largest allo w ed TW is that once TW is to o large,
it is imp ossible to extract equal n um b ers of LK data samples as LC. If TW is greater
than
t 0 − t ′
end
, LC dataset cannot ev en b e extracted. If c ho osing the smallest TW, i.e.
TW = 1 s, it still cannot satisfy the ab o v e criterion, then this LC will b e excluded for
data extraction.
100

6.4 Metho d
Ego vehicl e
TW length
𝑡 0
lane-keep
LK dataset LC dataset
same length
with TW
5s
4s
3s
2s
1s

(a)
An example of using TWL metho d to lab el LC and LK datasets. The n um b er
of lab eled LC samples and LK samples is equal.
TW length
𝑡 0
LK dataset L C dataset
𝑡 0 ′
TW length
T rajectory of the ego- vehicle
𝑡 𝑒𝑛𝑑 ′

(b) The criterion of c ho osing the largest length of TW for certain LC case.
Figure 6.17: Demonstration of using the TWL metho d for b oth LLC and RLC case.
101

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
Data lab eling result
F ollo wing the rules of the GBL and the TWL metho d, the n um b ers of the balanced
training samples is illustrated in T able 6.3. What needs to b e men tioned is, as w e can
see from the TWL metho d, the smaller the time-windo w is, the less the n um b er of
lab eled samples. In addition, the n um b er of the training samples b y the GBL metho d is
similar with the TWL metho d with 3 s time-windo w. The main difference of the GBL
and the TWL is that the GBL metho d tends to lab el LC data case b y case, how ev er,
the TWL metho d tak es all the LC cases as the same b y using a fixed time-windo w.
T able 6.3: The lab eled training samples b y the GBL and TWL metho d.
Lab eling metho d Data t yp e Scenario Scenario
LLC RLC
GBL LC 12001 12971
LK 12001 12971
TWL 5 s LC 17750 17500
LK 17750 17500
TWL 4 s LC 15500 16400
LK 15500 16400
TWL 3 s LC 12600 13350
LK 12600 13350
TWL 2 s LC 9350 9350
LK 9350 9350
TWL 1 s LC 4950 4925
LK 4950 4925
102

6.4 Metho d
6.4.2 F eature selection
The same rule is applied to feature selection as it is men tioned in section 5.6.1, i.e.
features whose
p
-v alue smaller than 0.05 at the same time Cohen’s
d
larger than 0.8 are
selected for mo del training. In statistical analysis,
p
-v alue < 0.05 represen ts statistical
significance. Similarly , Cohen’s
d
> 0.8 indicates high effect size lev el (J.Cohen, 1992).
In addition, for the same feature with differen t time-windo ws, feature with a larger time-
windo w is preferable b ecause more information is included within larger time-windo w.
F or instance, if b oth
max _ y aw 5
t
(5 s time-windo w) and
max _ y aw 3
t
(3 s time-windo w)
are qualified, max _ y aw 5
t is selected.
The final selected features for b oth LLC and RLC case as w ell as b y differen t lab eling
metho ds can b e seen in T able 6.4 and T able 6.5 (features with
✗
are the selected). The full
scale of
p
-v alue and Cohen’s
d
of all the extracted features are listed in App endix T able
C.2 and T able C.3. F rom the t w o tables w e can see that the final selected features
are differen t b y lab eling metho ds. A dditionally , LLC and RLC scenario also sho w
differen t feature selection results. This result is coinciding with the one w e concluded
in section 5.6.2 from the big data analysis, whic h emphasizes the imp ortance of doing
feature selection.
6.4.3 T raining dataset
After feature selection, training datasets can b e organized based on the selected features.
Three kinds of training datasets are prepared for mo del training:
•
Driving st yle dataset: training datasets are group ed b y driving st yles. In other
w ords, data collected from the participan ts whose driving st yles is in the same
group, i.e. high aggressiv e, medium aggressiv e or lo w aggressiv e, are categorized
together. The classified driving st yle of each participan t is listed in T able 6.2.
•
P ersonalized dataset: datasets are separated b y eac h participan t, whic h means
that eac h participan t has his/her individual training dataset. The aim of using the
p ersonalized datasets for training is to consider the individual driving st yle. As it
is men tioned in c hapter 4 that ev en t w o driv ers are at similar aggressiv e lev el, they
ma y sho w differen t temp orary driving st yles whic h can b e represen ted b y different
sp eed c hoice as w ell as accelerating or braking b eha vior.
•
Non-categorized dataset: all the datasets are directly group ed together without
considering driving st yle and individuation. It is a sup er h uge dataset.
103

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
T able 6.4: The feature selection results of differen t lab eling metho ds for LLC scenario.
# F eature TWL
GBL 5 s 4 s 3 s 2 s 1 s
1 y aw Rate t – – – ✗ – –
3 TTC − 1
t ✗ ✗ ✗ – ✗ ✗
13 std _ y aw 5
t ✗ ✗ ✗ ✗ ✗ ✗
18 max _ y aw 5
t ✗ ✗ ✗ ✗ ✗ ✗
23 min _ y aw 5
t – ✗ ✗ ✗ – –
24 min _ y aw 4
t – – – – ✗ –
26 min _ y aw 2
t ✗ –––––
27 min _ y aw 1
t – – – – – ✗
28 med _ y aw 5
t – ✗ ✗ ✗ – –
30 med _ y aw 3
t – – – – ✗ –
31 med _ y aw 2
t – – – – – ✗
32 med _ y aw 1
t ✗ –––––
33 mean _ a 5
t ✗ ✗ ✗ ✗ ✗ –
34 mean _ a 4
t – – – – – ✗
38 std _ a 5
t ✗ ✗ ✗ ✗ ✗ ✗
43 max _ a 5
t ✗ ✗ ✗ ✗ ✗ –
48 min _ a 5
t – ✗ ✗ ✗ – –
52 min _ a 1
t – – – – – ✗
53 med _ a 5
t – ✗ ✗ ✗ – –
54 med _ a 4
t – – – – ✗ –
58 mean _ br pd 5
t – ✗ ✗ ✗ ✗ –
60 mean _ br pd 3
t ✗ –––––
61 mean _ br pd 2
t – – – – – ✗
113 std _ stw 5
t ✗ ✗ ✗ ✗ ✗ ✗
118 max _ stw 5
t ✗ ✗ ✗ ✗ ✗ ✗
123 min _ stw 5
t – ✗ ✗ ✗ – –
125 min _ stw 3
t – – – – ✗ –
126 min _ stw 2
t ✗ –––––
127 min _ stw 1
t – – – – – ✗
128 med _ stw 5
t – ✗ ✗ ✗ – –
130 med _ stw 3
t – – – – ✗ –
131 med _ stw 2
t – – – – – ✗
132 med _ stw 1
t ✗ –––––
133 mean _ stw R ate 5
t – ✗ ✗ ✗ – –
134 mean _ stw R ate 4
t ✗ – – – ✗ ✗
163 max _ F _ a 5
t ✗ ✗ ✗ ✗ ✗ ✗
168 max _ F _ br pd 5
t ✗ ✗ ✗ ✗ – ✗
183 max _ F _ stw R ate 5
t – ✗✗✗✗✗
104

6.4 Metho d
T able 6.5: The feature selection results of differen t lab eling metho ds for RLC scenario.
# F eature TWL
GBL 5 s 4 s 3 s 2 s 1 s
1 y aw Rate t – ✗ ✗ ✗ – –
3 TTC − 1
t ✗ – – – ✗ ✗
13 std _ y aw 5
t ✗ ✗ ✗ ✗ ✗ ✗
18 max _ y aw 5
t ✗ ✗ ✗ ✗ ✗ –
19 max _ y aw 4
t – – – – – ✗
23 min _ y aw 5
t – ✗ ✗ ✗ – –
24 min _ y aw 4
t – – – – ✗ –
26 min _ y aw 2
t ✗ – – – – ✗
28 med _ y aw 5
t – ✗ ✗ ✗ – –
29 med _ y aw 4
t ✗ – – – ✗ –
31 med _ y aw 2
t – – – – – ✗
33 mean _ a 5
t ✗ ✗ – ✗ ✗ ✗
34 mean _ a 4
t – – ✗ – – –
38 std _ a 5
t ✗ ✗ ✗ ✗ ✗ ✗
43 max _ a 5
t ✗ ✗ ✗ ✗ ✗ –
45 max _ a 3
t – – – – – ✗
48 min _ a 5
t – ✗ ✗ ✗ – –
50 min _ a 3
t ✗ –––––
53 med _ a 5
t – ✗ ✗ ✗ – –
55 med _ a 3
t – – – – ✗ –
56 med _ a 2
t ✗ –––––
58 mean _ br pd 5
t ✗ ✗ ✗ – ✗ –
59 mean _ br pd 4
t – – – – – ✗
113 std _ stw 5
t ✗ ✗ ✗ ✗ ✗ ✗
118 max _ stw 5
t ✗ ✗ ✗ ✗ ✗ ✗
123 min _ stw 5
t – ✗ ✗ ✗ – –
124 min _ stw 4
t ✗ –––––
125 min _ stw 3
t – – – – ✗ –
127 min _ stw 1
t – – – – – ✗
128 med _ stw 5
t – ✗ ✗ ✗ – –
130 med _ stw 3
t ✗ – – – ✗ –
132 med _ stw 1
t – – – – – ✗
133 mean _ stw R ate 5
t – ✗ ✗ ✗ – –
134 mean _ stw R ate 4
t ✗ – – – ✗ –
136 mean _ stw R ate 2
t – – – – – ✗
163 max _ F _ a 5
t ✗ ✗ ✗ ✗ ✗ ✗
168 max _ F _ br pd 5
t ✗ ✗ ✗ ✗ ✗ ✗
169 max _ F _ br pd 4
t – – – – –
183 max _ F _ stw R ate 5
t ✗ ✗ ✗ ✗ ✗ ✗
105

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
6.5 Ev aluation result
In this section, ev aluation is made from differen t asp ects, i.e. mo del selection, training
datasets comparison, lab eling metho d comparison as w ell as the real-time prediction
test. The metho d used for ev aluation adopts the same rule as it is used in section 4.6
where R OC curv es and A UC v alues are as the metrics. Similar metho ds can b e found in
McCall et al. (2007), Liebner et al. (2013), P eng et al. (2015), Doshi and T riv edi (2009),
and Lethaus, Baumann, et al. (2013).
6.5.1 Mo del and dataset comparison
In this section, three mac hine learning mo dels prop osed in section c hapter 4 are ev aluated.
They are lane-c hange Ba y esian net w ork with a Gaussian mixture mo del (LCBN-GMM),
SVM and naiv e Ba y es (NB) mo del. LCBN-GMM has the same structure as it is detailed
in section 4.5.1. The prior distribution of NB is set as Gaussian. F or SVM, the k ernel
function used to generate the decision b oundary is mo deled as a Gaussian k ernel. The
ab o v e three mo dels are trained b y the same training datasets men tioned in the last
section and th us w e can compare the classification p erformance. The p erformance of
classifying LC and LK data samples b y each model is listed in T able 6.6, where the
results are after 10-fold cross-v alidation. The corresp onding R OC curv es can b e found
in App endix Figure C.3.
T able 6.6:
The A UC v alues of LCBN-GMM, SVM and NB trained b y differen t datasets
using GBL metho d .
Scenario T raining dataset LCBN-GMM SVM NB
LLC
Driving st yle 0.9659 0.9962 0.8340
P ersonalized 0.9877 0.9970 0.9247
Non-categorized 0.8215 0.9900 0.7825
RLC
Driving st yle 0.9673 0.9987 0.8190
P ersonalized 0.9892 0.9981 0.9203
Non-categorized 0.8577 0.9964 0.7611
The results can b e discussed as follo ws.
•
Comparison of mo dels: LCBN-GMM and SVM p erform m uc h b etter than NB in
b oth LLC and RLC scenarios trained b y eac h dataset. SVM p erforms sligh tly
b etter than LCBN-GMM (A UC v alues in b old are the b est among eac h mo del).
•
Comparison of training datasets: using the p ersonalized training dataset, mo dels
can ac hiev e b etter p erformance than using the driving st yle dataset and m uc h
b etter than the non-categorized dataset. The only exception is for SVM in RLC
106

6.5 Ev aluation result
scenario where using the driving st yle dataset it p erforms sligh tly b etter than
using the p ersonalized dataset. The non-categorized datasets are the w orst for all
the mo dels. In addition, w e find that SVM is not influenced hea vily b y training
datasets as LCBN-GMM and NB.
In conclusion, NB and the non-categorized datasets are not qualified since their
p erformances are far w orse than other com binations and th us w ould not b e tak en into
accoun t for further comparison.
6.5.2 Lab eling metho d comparison
In mo del comparison and training dataset comparison, NB and the non-categorized are
out. This section w e use LCBN-GMM and SVM to compare the p erformance of using
the driving st yle dataset and the p ersonalized dataset b y differen t lab eling metho ds. The
A UC v alues of LCBN-GMM and SVM are listed in T able 6.7 and T able 6.8, resp ectiv ely .
The corresp onding R OC curv es can b e seen in App endix Figure C.4, where the results
are after 10-fold cross-v alidation.
T able 6.7:
The A UC v alues of LCBN-GMM trained b y different datasets using both
GBL and TWL metho d.
Scenario Lab eling metho d Driving st yle P ersonalized
LLC
GBL 0.9659 0.9877
TWL 5 s 0.9618 0.9867
TWL 4 s 0.9578 0.9843
TWL 3 s 0.9054 0.9767
TWL 2 s 0.8856 0.9699
TWL 1 s 0.8929 0.9706
RLC
GBL 0.9673 0.9892
TWL 5 s 0.9260 0.9853
TWL 4 s 0.9220 0.9873
TWL 3 s 0.9050 0.9806
TWL 2 s 0.9274 0.9823
TWL 1 s 0.9044 0.9608
107

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
T able 6.8:
The A UC v alues of SVM trained b y differen t datasets using b oth GBL and
TWL metho d.
Scenario Lab eling metho d Driving st yle P ersonalized
LLC
GBL 0.9962 0.9970
TWL 5 s 0.9970 0.9972
TWL 4 s 0.9964 0.9964
TWL 3 s 0.9971 0.9975
TWL 2 s 0.9919 0.9947
TWL 1 s 0.9923 0.9941
RLC
GBL 0.9987 0.9981
TWL 5 s 0.9982 0.9984
TWL 4 s 0.9982 0.9975
TWL 3 s 0.9966 0.9964
TWL 2 s 0.9964 0.9948
TWL 1 s 0.9896 0.9919
F rom the t w o tables w e find the follo wing results:
•
Comparison of lab eling metho d: except for SVM in LLC scenario where using the
TWL metho d with 3 s time-windo w, the b est A UC v alues are achiev ed b y using
the GBL metho d.
•
Comparison of training datasets: except for SVM in RLC scenario, the b est A UC
v alues are all from the p ersonalized datasets.
In conclusion, training the mo dels using the p ersonalized datasets lab eled b y the GBL
metho d is the recommended com bination. Th us for the final real-time LC prediction
test, w e test b oth LCBN-GMM and SVM using the p ersonalized datasets with the GBL
metho d.
6.5.3 Real-time p erformance ev aluation
In order to ev aluate the real-time LC prediction p erformance of LCBN-GMM and SVM,
data from the en tire driv e of eac h participan t are fed to the off-line trained mo del, i.e.
b y the p ersonalized training datasets with the GBL metho d, to test the real-time LC
prediction p erformance. One thing needed to b e men tioned is that b ecause of the sensor
failure during exp erimen t, part of the data collected from participan t # 4 are missing
and th us will not b e used for testing.
Unlik e the off-line training and testing task whic h only p erforms classification giv en
the lab eled LC and LK data samples, the real-time prediction task is m uc h more
108

6.5 Ev aluation result
c hallenging. The reason is that in the real-time prediction test the whole data in time
series are tested. It includes significan t amoun t of untrained data samples and the
ma jorit y of them are LK data samples. Th us, reducing false alarm and at the same
time ac hieving high precision is a big c hallenge.
Prediction fusing ey e-trac king signal
In the real-time prediction test, the ey e-trac king signal is fused in to the prediction
algorithm, whic h is defined in terms of glancing_r atio by
glancing _ ratio = mirror glancing duration
TW (6.3)
where the mirror-glancing duration is the total amoun t of time b y the participan t to
glance at the left view mirror and the left windo w (for RLC case is the righ t view mirror
and the righ t windo w). The time-windo w here is chosen as 5 s. If the participan t do es
not c hec k the left/righ t view mirror/windo w, this ratio should b e zero. The non-zero
histogram of glancing _ ratio for b oth LLC and RLC is plotted in Figure 6.18.
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
glancing ratio
0
1000
2000
3000
4000
5000
6000
7000
8000
LLC
RLC

Figure 6.18: Non-zero histogram of mirror glancing ration for LLC and RLC scenario.
109

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
Algorithm
The algorithm of the real-time LC prediction is describ ed as follo w:
Algorithm 1 Algorithm of the real-time driv er LC b eha vior prediction.
1: Input feature signals at time t .
2: if P ( LLC ) > P ( R LC ) then
3: if P ( LLC ) ≥ ϵ & glancing _ ratio ≥ ξ l ef t then
4: LLC
5: else Lane-k eeping
6: end if
7: else if P ( LLC ) < P ( R LC ) then
8: if P ( RLC ) ≥ ϵ & glancing _ ratio ≥ ξ r ig ht then
9: RLC
10: else Lane-k eeping
11: end if
12: else
13: Lane-k eeping
14: end if
where,
ϵ
is the decision threshold whic h is set as 0.9 for LCBN-GMM and 0 for
SVM 23 . ξ lef t and ξ r ig ht are set as 0.05 and 0.03, resp ectiv ely .
Ev aluation
Three metrics, i.e. precision, recall and predicted time ahead of
t 0
, are used to ev aluate
mo del p erformance. Precision and recall are defined as:
P r ecision = T P
T P + F P
Recal l = T P
T P + F N
(6.4)
where TP , FP and FN are true p ositiv es, false p ositiv es and false negativ es, resp ectiv ely .
F or the case of LC b eha vior prediction, TP , FP and FN is defined as follo ws and can b e
illustrated in Figure 6.19:
•
TP: if the algorithm rep orts LLC/RLC in ten t and within the next 8 seconds
24
the
driv er do es execute a LLC/RLC maneuv er, then this prediction is coun ted as a TP
otherwise it is regarded as a FN.
23
Since Ba y esian netw ork is a probabilit y mo del, this threshold means that the mo del has 90%
confidence for the prediction. F or SVM, it simple classifies data b y the decision b oundary , where the
p ositiv e class is greater than zero and negativ e class is smaller than zero
24
The reason of c ho osing this threshold is that from Figure 6.16 w e can see that the ma jorit y of
t pr epar e
are within the range of 0 - 6 s and the maxim um is nearly 8 s. Thus the maxim um
t pr epar e
, i.e.
8 s, is selected as the threshold. F or reference, in Leonhardt, P ec h, and W anielik (2018) the threshold
is set as 10 seconds, whic h means a larger forgiv eness.
110

6.5 Ev aluation result
•
FP: if the driv er executes LLC/RLC maneuv er but in the previous 8 seconds the
algorithm did not rep ort the correct LLC/RLC or there is ev en no rep ort at all,
then this case is coun ted as a FP .
𝑡 0
LC report
P r edition
< 8 s
TP +1
LC report
P redition
= 8 s
FN +1
LC report
P redition
= 8 s
FP +1
LC report
= 8 s
FN +1 FN +1
No LC report
𝑡 0
𝑡 0

Figure 6.19: Illustration of TP , FN and FP of the real-time prediction.
Precision and recall are coun ted separately for LLC and RLC case. F or comparison,
w e also p erform the real-time prediction test for the case of without fusing ey e-trac king
signal as w ell as only using ey e-trac king signal. The threshold setting is the same as it
is in Algorithm 1. The final results are illustrated in T able 6.9, where the v alue in b ond
represen ts the b est p erformance giv en metrics.
T able 6.9: The real-time LC prediction result p erformed b y LCBN-GMM and SVM.
Metric F usion of ey e-trac king Only Only
LCBN-GMM SVM LCBN-GMM SVM ey e-trac king
LLC
Precision 89.42% 93.51% 93.03% 98.21% 92.23%
Recall 56.34% 54.09% 26.71% 31.78% 42.59%
Prediction 3.1 s 3.3 s 4.7 s 5.1 s 3.6 s
time
RLC
Precision 71.21% 72.39% 94.40% 97.61% 62.78%
Recall 48.70% 82.90% 26.95% 31.78% 63.34%
Prediction 2.6 s 2.6 s 4.7 s 4.9 s 2.8 s
time
111

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
F rom the table w e find the following results:
•
Comparison of LCBN-GMM and SVM fusing ey e-trac king signal: SVM ac hiev es
sligh tly higher precision in b oth LLC and RLC scenario and m uc h b etter recall v alue
in RLC scenario. The recall of LCBN-GMM in LLC scenario is sligh t higher than
SVM. In addition, SVM can predict LC b eha vior sligh t earlier than LCBN-GMM.
•
Comparison of fusion and without fusion of ey e-trac king signal: for LLC, the
prediction of b oth LCBN-GMM and SVM fusing ey e-trac king signal are slightly
lo w er (around 5%) than without fusing ey e-trac king signal. The reason is that b y
fusing ey e-trac king signal, an additional condition is added to predict a LC rep ort,
whic h means the condition of rep orting a true LC is stricter. This can increase
the c hance of missing-prediction. The missing-prediction case is more often in
RLC case, whic h leads to a lo w er precision. The p ossible reason is that fusing
ey e-trac king signal is highly dep ended on if the driver performs mirror-glancing
b eha vior b efore LC. And in RLC case, based on the statistics in T able 6.2, the
c hance of executing a LC without mirror-glancing b eha vior in RLC scenario is
nearly 50% more than LLC scenario. This is wh y the precision of predicting RLC
b y fusing ey e-trac king signal is muc h lo w er than LLC.
Ho w ev er, the b enefit of more strict condition is highly increasing the recall. W e
can see that the recall v alues of the fusing ey e-trac king signal of b oth mo dels are
m uc h higher than without fusing ey e-tracking signal, whic h are nearly 30% for
LCBN-GMM and 20% for SVM (in RLC case for SVM is more than 50%). a Higher
recall v alue represen t a lo w er false alarm lev el, whic h pla ys an essen tial role in the
practical implemen tation of AD ASs.
•
Comparison of fusion ey e-trac king and only using ey e-trac king signal: prediction of
LLC b y only using ey e-trac king signal can achiev e relativ ely higher precision with
earlier prediction time, ho w ev er, the recall is more than 10% lo w er than fusion
metho d. F or RLC, only using ey e-trac king signal suffers a lo w prediction. This is
also in consisten t with the result that glance pattern is promising LC predictors
for certain t yp es (Beggiato et al., 2018), implying that it do es not apply to all the
cases. Th us, it can b e concluded that only using ey e-tracking signal to predict
driv er LC b eha vior is not recommended.
In conclusion, balancing the precision and the recall, SVM fusing ey e-trac king signal
can ac hiev e the b est p erformance in LLC scenario. F or RLC scenario, among all the
metho ds, it is difficult to balance the precision and the recall, whic h indicates the
prediction of RLC is more c hallenging than LLC. The precision of SVM fusing ey e-
trac king signal decreases significan tly compared with its p erformance in LLC scenario.
But it could ac hiev e m uc h higher recall v alues than other coun terparts, whic h means
the false alarm is v ery lo w.
112

6.6 Summary
0 10 20 30 40 50 60 70 80 90 100
second
-1.5
-1
-0.5
0
0.5
1
1.5
glancing ratio left
glancing ratio right
svm prediction signal
actual LC t 0

Figure 6.20: An example of the real-time prediction of LC b y SVM.
Figure 6.20 giv es an example of the real-time prediction of LC b y SVM during 100
seconds. The red dotted line represen ts the momen t
t 0
in Figure 6.19. The p ositiv e
v alues are for LLC and negativ e v alues for RLC. The blue line is the SVM prediction
signal for prediction of LLC as 1 and RLC as -1. W e can see that SVM can predict
LC sev eral seconds b efore
t 0
for b oth LLC and RLC. The green and the pink line are
the
glancing _ ratio
in equation
(6.3)
for left and righ t side, resp ectiv ely . It depicts the
c hange of
glancing _ ratio
b efore and after LC, and it sho ws that
glancing _ ratio
has a
dramatic increase b efore eac h LC.
6.6 Summary
The w ork presen ted in this c hapter is the com bination of c hapter 4 and c hapter 5, aiming
to design a comprehensiv e framew ork of prediction of driv er LC b eha vior. The con ten t
includes exp erimen tal design, data pro cessing, feature selection, mo del selection and
ev aluation. Although the condition of the exp erimen t conducted in this c hapter is not
exactly the same as it is in c hapter 4 and c hapter 5, the metho ds are mainly the same.
In order to ev aluate the metho ds prop osed in the last c hapters, a real-road exp erimen t
w as conducted on high w a y in Berlin. T otally 12 participan ts joined in the exp erimen t
with 3 hours’ driv e for eac h p erson (40 min on high w a y). During the exp erimen t, the
participan ts w ere ask ed to wear the SMI ey e-trac king glasses to monitor their gaze
b eha vior. A seman tic gaze mapping metho d w as used to capture the A oIs of the
participan t during the driving task. Sync hronization w ork w as done b et w een CAN
Bus and the ey e-trac k er so that data collected from differen t sensors are sync hronized.
F urthermore for the off-line data pro cessing, all the data w ere re-sampled at 25Hz b y
using a in terp olation metho d. In preparing for training datasets, b esides the driving
113

6. Exp erimen t 2 - Ev aluation based on a real-road exp erimen t
st yle dataset and the non-categorized training datasets, a new training dataset, i.e. the
p ersonalized dataset, w as added for comparison.
The feature selection metho d used in this c hapter is the same as it is presen ted in
c hapter 5, where
p
- v alue and Cohen’s
d
w ere c hosen as the metrics. The result sho ws
that the items of the final selected features are differen t b y LC scenarios as w ell as b y
lab eling metho ds, whic h implies that feature selection is necessary b efore mo del training.
The ML mo dels b eing tested are the same as in c hapter 4, i.e. LCBN-GMM, SVM
and naiv e Ba y es. Data are lab eled using b oth the GBL and the TWL metho d. The
ev aluation applies to the same metrics used throughout this dissertation, i.e. R OC
curv es and A UC v alues. Result sho ws that the p ersonalized training dataset with the
GBL metho d is the b est com bination for mo del training. Comparison of the three
mo dels, LCBN-GMM and SVM outp erform NB, th us NB is out of the final real-time
prediction test.
In the final test, w e mimic the real-time prediction scenario b y feeding data from
the en tire driv e (participan t b y participan t) in time series to LCBN-GMM and SVM to
ev aluate their real-time prediction p erformance. Comparison w as also made b et w een
fusing and without fusing ey e-trac king signal as w ell as only using ey e-trac king signal
for prediction. Result sho ws that in LLC scenarios, SVM fusing ey e-trac king signal can
ac hiev e the b est p erformance. It can predict driv er LC b eha vior 3.3 s b efore actual
LC with precision 93.51% and recall 54.09%. How ev er, it represen ts differen t picture
in RLC scenario. It is difficult to balance the precision and the recall, whic h in other
w ords indicates that predicting driv er LC b eha vior in RLC scenario is more difficult
than in LLC scenario.
The limitation of this study is that b ecause there is no detection sensors equipp ed
in our testing v ehicle, no con textual traffic is mo deled. Th us, w e cannot analyze the
influence of differen t con textual traffic on mo del p erformance. Esp ecially for the fact
that LCBN-GMM p erforms b etter than SVM in c hapter 4 where the contextual traffic
is mo deled, ho w ev er, in this c hapter SVM sligh tly outp erforms LCBN-GMM without
mo deling the con textual traffic. In addition, since the case of the driv er making LC
without glancing mirror happ ens more often for RLC than LLC, whic h mak es it more
c hallenging to predict RLC. If the con textual traffic can b e mo deled, it could b e b eneficial
for the prediction. The influence of the con textual traffic on driv er LC b eha vior as w ell
as mo del p erformance will b e further discussed in next c hapter.
114

7
Discussion and outlo ok
This c hapter summarizes the final conclusions throughout this dissertation and giv es an
outlo ok on the researc h of prediction of driv er lane-change behavior.
7.1 Ov erall conclusion
In this dissertation, a systematic framew ork of prediction of driv er lane-c hange (LC)
b eha vior is prop osed, including exp erimen tal design, data pro cessing, data lab eling as
w ell as mo del p erformance ev aluation. Sev eral issues reading to the framew ork are
scattered in three studies, i.e. c hapter 4 - c hapter 6. The pip eline of the researc h metho d
is in the form of question and answer and can b e summarized as: What did the r elate d
works do? , What c an b e impr ove d? , What is our limitation and how to over c ome it? .
The conclusions from differen t asp ects are discussed in the follo wing sections.
7.1.1 Mo deling
Mo deling is the first issue to b e considered for implemen tation. The main mo deling
w ork that is included in this dissertation is regarding to mo deling of driving con textual
traffic and mac hine learning (ML) mo dels.
Con textual traffic
It has b een concluded in man y researc h that con textual traffic is v ery imp ort in driv er
b eha vior related studies (Oliv er and P en tland, 2000; W ahle et al., 2000; McGehee
et al., 2002; W ahle et al., 2002). In this dissertation, the concept of the con textual
traffic is related to the relationship b et w een the sub ject v ehicle and its surrounding
v ehicles. In the case of driv er LC b eha vior, the con textual traffic could impact on driv er
decision-making pro cess. In more complex con textual scenario, it tak es longer time for
115

7. Discussion and outlo ok
the driv er to prepare for a LC maneuv er. The con textual traffic also has influence on
driv er gaze b eha vior whic h is a v ery imp ortan t indicator for prediction of driv er LC
b eha vior.
In order to dynamically mo deling the con textual traffic, a cell-grid mapping metho d
is used inspired b y Do et al. (2017), Nilsson, Silvlin, et al. (2016), and Kasp er et al.
(2012). So far, this mo deling metho d is only implemen ted on high w a y roads since the
road situations are less complex than in cit y . In addition, this cell-grid mo deling metho d
is also dep ended on ho w man y lanes it has on high w a y road and the sensor lev el of the
exp erimen tal v ehicle. F or example, the n um b er of cells is differen t for t w o-lane high w a y
and three-lane high w a y . And if there are only fron t detection sensors, e.g. camera or
LiD AR, then only the fron t con textual traffic can b e mo deled.
In c hapter 4, where the exp erimen t w as conducted in a driving sim ulator, it is
assumed that all the necessary sensors are w ell equipp ed and th us w e could mo del b oth
the fron t and the bac k con textual traffic. In c hapter 5, where the ego-v ehicle had only
fron t view camera installed, th us only the fron t con textual traffic w as mo deled. Ho w ev er
in c hapter 6, limited b y no detection sensors, the real-road exp erimen t w as conducted
without con textual traffic mo deling. The absence of mo deling contextual traffic is also
one reason that leads to the relativ e p o or prediction p erformance of righ t LC (RLC)
in comparison to left LC (LLC). It is also indicated in Beggiato et al. (2018) that b y
monitoring v ehicle en vironmen t it can allo w for b etter prediction p erformance, e.g. the
n um b er of driving lanes a v ailable, the p ossibilit y of c hanging the lane (i.e. traffic densit y
on the target lane) and the presence of a slo w er leading v ehicle etc.
Mac hine learning mo dels
Sup ervised learning mo dels are mainly fo cused on in this dissertation. The t w o main
branc hes of sup ervised learning mo dels are generic mo del and discriminan t mo del. F or
the purp ose of co v ering b oth of the t w o branc hes, an easier generic mo del of Naiv e
Ba y es (NB), and a more complex generic mo del of Ba y esian net w orks (BN) as w ell
as discriminan t mo del of supp ort v ector mac hine (SVM) are implemen ted for mo del
comparison. BN is mo deled sp ecially for LC case and is incorp orated with a Gaussian
mixture mo del in terms of LCBN-GMM.
By comparing the classification p erformance of these three mo dels in b oth c hapter 4
(a driving sim ulator based study) and c hapter 6 (a real-road exp erimen t based study), it
can b e concluded that LCBN-GMM and SVM outp erforms NB. The p o or p erformance
of NB ma y b e caused b y the conditional indep enden t assumption b et w een features. This
is the biggest do wnside of NB. Ho w ev er, the comparison of LCBN-GMM and SVM
in c hapter 4 and c hapter 6 indicates differen t results. In the driving sim ulator based
exp erimen t, LCBN-GMM p erforms b etter than SVM, ho w ev er, in the real-road based
exp erimen t SVM p erforms sligh t b etter than LCBN-GMM. This con tro v ersial result ma y
b e coursed b y the differen t exp erimen tal condition. Because the real-road exp erimen t
116

7.1 Ov erall conclusion
w as conducted without con textual traffic mo deling, this mak es LCBN-GMM not p erform
at its b est as it could. In addition, b ecause SVM is a discriminan t mo del, whic h do es not
care ho w the data are generated but simply tries to find the b est classification b oundary
to classify data samples, it is less sensitiv e to the qualit y of the dataset compared with
generic mo dels.
Although it is still an op en question whic h really p erforms b etter, it can b e concluded
that b oth LCBN-GMM and SVM could ac hiev e high p erformance using the prop osed
metho d in this dissertation. In the real-road exp erimen t, the b est A UC v alues of LCBN-
GMM is nearly 0.99 for b oth LLC and RLC scenario, and SVM could ac hiev e sligh t
o v er 0.99 in b oth LLC and RLC scenario. Based on the study b y F an, Upadh y e, and
W orster (2006), A UC > 0.97 can b e regarded as a v ery go o d classification p erformance.
7.1.2 F eature selection
F eature selection is crucial for ML mo dels. It can not only sp eed up the learning
pro cess but also can impro v e mo del p erformance (Kira and Rendell, 1992). A statistical
metho d is prop osed in this dissertation to select the most con tributiv e features based
on the metrics of
p
-v alue and Cohen’s
d
. Instead of ranking features to a sp ecific
algorithm (Geng et al., 2007), this feature selection metho d tends to a v oid heuristic
searc h and th us the selected features can b e used b y all the ML mo dels.
F rom the big data analysis in c hapter 5, it can b e concluded that the imp ortance of
the features is v arying b y differen t con textual scenarios. In other w ords, to predict driv er
LC b eha vior in differen t scenarios, the selected strong features are probably differen t
as w ell. F requency-domain features, whic h are rarely used in driv er b eha vior related
researc h, are also promising features, with nearly at least one feature b eing selected
as the strong feature in eac h scenario. In addition, in some con textual scenarios, the
commonly assumed and b eing used features regarding to v ehicle lateral mo v emen t, e.g.
lateral acceleration and time-to-lane cross (TLC), do not sho w statistical significance
and th us w ould not b e regarded as strong features. This result indicates that feature
selection should b e done in a systematic w a y rather than only based on the empirical
kno wledge.
After feature selection, 95 features are reduced to around 10 features for eac h scenario.
Finally , ML mo des are tested with the use of the selected features and all the features
without selection. The result suggests that using the selected features, the p erformance
of ML mo dels sho ws increase in differen t lev els (maxim um 10% increase) compared
with using all the features. The exception is NB mo del, whose p erformance can hardly
b e impro v ed. Considering the fact that using the selected features (nearly only 10
features), ML mo dels (except for NB) can still ac hiev e the same p erformance or ev en
ha v e impro v emen ts, it can b e concluded that it is more efficien t and effectiv e to use the
selected features than use all the features without feature selection.
117

7. Discussion and outlo ok
7.1.3 Ev aluation
The main metrics used for ev aluating classification p erformance are R OC curv e and A UC
v alue, whic h are widely used in man y related w orks lik e in McCall et al. (2007), Liebner
et al. (2013), Peng et al. (2015), Doshi and T rivedi (2009), and Lethaus, Baumann,
et al. (2013). W e also use precision and recall as the metrics to ev aluate the real-time
prediction p erformance. Ev aluations are made in the follo wing asp ects.
T raining dataset and Lab eling metho d
In the last section w e ha v e discussed the imp ortance of feature selection. A ctually , the
qualit y of the training datasets can also impact on the p erformance of the ML mo dels.
Although feature selection is considered in man y researc h to impro v e mo del
p erformance, few researc h studied the influence of training dataset on prediction
p erformance. As w e men tioned the influence of driving style on driv er LC b eha vior,
it mainly affects the qualit y of the lab eled training datasets. T o impro v e the qualit y
of the training datasets, datasets are separated in to differen t categories to test their
p erformance, i.e. driving st yle datasets (datasets are group ed based on driving st yles),
p ersonalized datasets (data collected from eac h participan t is a single group) and
non-categorized datasets (a h uge dataset without an y classification).
Another issue can ha v e influence on the qualit y of the training datasets is the lab eling
metho d. The most p opular w a y to lab el LC and LK datasets b y the related researc h is
the time-windo w lab eling (TWL) metho d, with the assumption that data within certain
time-windo w (TW) ahead of the LC maneuv er can b e lab eled as LC samples (Mandalia
and Salvucci, 2005; Doshi and T riv edi, 2009; Lethaus, Baumann, et al., 2013; Doshi
and T riv edi, 2008; Morris, Doshi, and T riv edi, 2011). The limitation of this lab eling
metho d is without considering the differences b et w een driv ers and LC cases. Because
driv er LC b eha vior is v ery driv er and LC case sp ecific. Considering these differences, a
gaze-based lab eling (GBL) metho d is prop osed with the use of ey e-trac k er. The GBL
metho d considers the mirror glancing b eha vior of the driv er b efore an actual LC. All
the training datasets are lab eled b y b oth the GBL and the TWL metho d for ev aluation.
Result sho ws that using the driving st yle training dataset is m uc h b etter than the
non-categorized training dataset. The p ersonalized training dataset p erforms sligh tly
b etter than the driving st yle dataset. This result implies that the effect of driving st yle
on mo del p erformance do es exist. Th us, preparing for the training datasets b y individual
driv er is preferable. What it needs to b e men tioned here is that the p ersonalized
training datasets ma y suffer from b eing short of training samples. This is the reason
that in c hapter 4 there are no p ersonalized training datasets b eing considered. F or
inadequate training samples, organizing dataset b y driving st yles is recommended since
it outp erforms the non-categorized dataset. In addition to the comparison b et ween
lab eling metho d, the GBL ac hiev es the b est p erformance for b oth LCBN-GMM and
118

7.1 Ov erall conclusion
SVM almost in all scenarios, except for SVM in LLC scenario where the TWL p erforms
sligh tly b etter than the GBL with 3 s time-windo w.
In conclusion, the b est com bination used for mo del training is the p ersonalized
training datasets with the GBL metho d.
Real-time prediction p erformance
All the w ork presen ted ab o v e, i.e. mo deling, feature selection, mo del selection as w ell as
training dataset and lab eling metho d selection, is to pa v e the w a y to ev aluate the real-
time prediction p erformance. Both LCBN-GMM and SVM trained b y the p ersonalized
datasets using the GBL metho d are tested. In the real-time test, w e feed all the data
(on high w a y) in time series to the tw o trained mo dels and to see whether they can
correctly predict LC maneuv ers or not. Comparison is made b et w een fusing and without
fusing ey e-trac king signal in the prediction algorithm as w ell as only using ey e-trac king
signal without ML mo dels for prediction. Precision and recall are the t w o metrics for
ev aluation. Precision represen ts the lev el of ho w man y LC b eha viors can b e correctly
predicted, whereas recall reflects the false alarm lev el.
Comparison b et w een LCBN-GMM and SVM b oth fusing ey e-trac king signal, SVM
p erforms sligh tly b etter than LCBN-GMM for LLC case and m uc h b etter for RLC case
b y ac hieving significan t higher recall v ales. Th us, SVM fusing ey e-trac king signal is
c hosen for further comparison.
F or LLC case b y SVM fusing ey e-trac king information, although the precision
decreases sligh tly in comparison to without fusing eye-trac king signal, the recall can b e
impro v ed significan tly . The reason of fusing ey e-trac king mak es the precision sligh tly
decreases is that an additional threshold mak es it stricter to rep ort a LC. But it can
also reduce false alarm significan tly . Giv en that fusing ey e-trac king signal the mo dels
can still ac hiev e high precision (93.5%) and with significan t recall increase, it can b e
concluded that fusing ey e-trac king signal can impro v e the prediction p erformance. And
b y comparing fusing ey e-trac king signal and only using ey e-trac king signal for prediction,
w e found that SVM fusing ey e-trac king signal outp erforms only using ey e-trac king signal
in b oth precision and recall.
F or RLC case, ho w ev er, it sho ws differen t picture. Only using ey e-trac king signal for
prediction could lead to p o or p erformance. And it is difficult to balance precision and
recall b y either fusing ey e-trac king signal or not. F or SVM without fusing ey e-trac king
signal, it can ac hiev e high precision but suffer from lo w recall, whic h means high false
alarm. F usion of ey e-trac king signal, the precision of SVM is going do wn significan tly
despite the recall also increases significan tly . This result indicates that predicting driv er
righ t LC b eha vior is more difficult than left LC b eha vior. The p ossible reason is that w e
can observ e less evidence in RLC case than LLC case. Researc h found that most lane
c hanges are to the left with a mean duration of o v er 11 seconds than to the righ t whic h
has a mean of 6.6 seconds (Lee, Olsen, Wierwille, et al., 2004). LLC cases are mainly
119

7. Discussion and outlo ok
for the purp ose of o v ertaking a slo w leading v ehicle. Esp ecially based on traffic rules in
German y , it is not allo w ed to execute a RLC for o v ertaking. In an o v ertaking pro cess,
the driv er has to do more preparation to execute a LLC and th us giv es more clues that
can b e used for prediction. F or example, turn signal used in LLC case is 13% more
frequen tly than RLC case, and the c hance of glancing at the left view mirror for LLC
is also higher than glancing at the righ t view mirror for RLC (Lee, Olsen, Wierwille,
et al., 2004). It w as h yp othesized that the driv er executing RLC has often just passed a
slo w leading v ehicle and therefore has a greater degree of situational a w areness than the
driv er an ticipating a LLC. Th us, the driv er ma y feel that it is unnecessary to do the
same safet y c hec k for RLC (Lee, Olsen, Wierwille, et al., 2004).
In conclusion, SVM fusing ey e-trac king signal can ac hiev e go o d prediction p erfor-
mance for LLC case, ho w ev er for R CL case, more driving uncertain t y , e.g. sp ecified
con textual traffic, p ossibility of c hanging the lanes, should b e kno wn in adv ance to
predict LC more precisely and reliably .
7.1.4 Con tribution
Although the topic of prediction of driv er LC b eha vior has b een studied in man y related
w orks for a couple of y ears, some metho ds prop osed in this dissertation are original and
can b e extended to the other similar fields of researc h. The original con tributions of
this dissertation are summarized as follo ws:
1.
Although the metho d of using a cell-grid to mo del the con textual traffic and the
w a y of using a b eha vioral-psyc hological questionnaire to classify diving st yle are
from related researc h, the metho d of considering these t w o factors in preparing for
the training datasets is unique.
2.
A no v el data lab eling metho d termed as the GBL metho d is prop osed. This lab eling
metho d tak es adv an tage of driv er gaze b eha vior and can mak e LC data lab eling
w ork correlated with driv er LC b ehavior. In this w ay the qualit y of the training
datasets can b e impro v ed.
3.
Regarding to prediction of driv er LC b eha vior, few researc h ha v e done with
a systematic feature selection w ork b efore training their ML mo dels. The
most common case is just using features based on the empirical kno wledge or
recommended in the prior researc h rather than p erforming a comprehensiv e feature
selection w ork. This dissertation prop oses a systematic feature selection metho d in
the p ersp ectiv e of statistics. Wide ranges of features related to driv er LC b eha vior
are extracted, e.g. v ehicle dynamic features, driv er b eha vior features, com bined
features and time-windo w features, to enric h the feature sets. In addition, features
are not limited in time-domain but also co v ering frequency-domain. This feature
extraction and selection metho d is general for all the ML mo dels and can b e also
extended to other field of researc h regarding to mac hine learning tec hniques.
120

7.2 Outlo ok
7.2 Outlo ok
Mo dern AD ASs need to adapt to driv er in ten tions and situations to matc h the driv er’s
actual need for assistance, that is to sa y the future AD ASs themselv es need the kno wledge
ab out the driv er’s in ten tion (Leonhardt, P ec h, and W anielik, 2018). F or the case of the
LC assistance system, the framew ork presen ted in this dissertation is v ery promising in
practical application. In the sim ulated real-time prediction test, b y using the metho d
prop osed in this dissertation, driv er LC b eha vior can b e predicted on av erage 3.3 s
ahead of an actual LC maneuv er for LLC case and 2.6 s for RLC case. There are mainly
t w o use cases for the prediction:
1.
The prediction signal can b e implemen ted in AD AS b y either activ e feedbac k or
passiv e feedbac k. F or the activ e feedbac k, when the system predicts the driv er w an ts
to mak e LC, then AD AS could assist the driv er with recommended acceleration
and sp eed as w ell as the LC path etc. And for the passiv e feedback, AD AS can
alarm the driv er if the LC in ten tion is unsafe in curren t driving situation.
2. The tec hnology of the connected v ehicles plays a k ey role in realizing co op erativ e
in telligen t transp ortation systems (Narla, 2013). The prediction signal can b e used
for in ter-v ehicle comm unication b y sharing it with other surrounding v ehicles and
can th us co op erativ ely prev en t the p otential traffic acciden ts.
Although this dissertation prop oses a comprehensiv e framew ork for prediction of
driv er LC b eha vior, it still has a long w a y to go for the real-road implementation. The
follo wing asp ects should b e particularly tak en in to account.
•
Driving uncertain t y: b eing maximal a w are of the driving uncertain ty is the
goal, since without understanding of the driving uncertain ties ma y lead to
prediction failure as it is discussed in RLC case. In this dissertation, w e
can conclude that mo deling con textual traffic is helpful for prediction of LC.
Ho w ev er, more uncertain ties, e.g. the p ossibilit y of c hanging the lanes, lane
mark information (Leonhardt, P ec h, and W anielik, 2018), and even w eather or
illumination condition, are also necessary to b e co v ered.
•
Sync hronization: the imp ortance of this issue is usually underestimated. In this
dissertation, an off-line sync hronization metho d is prop osed b y re-sampling the
data with in terp olation. Ho w ev er, it is m uc h more c hallenging to sync hronize
differen t sensors in real-time case (Alemdar and Ibnkahla, 2007). The Net w ork
Time Proto col (NTP) (Mo c k et al., 2000) or reference-broadcast sync hronization
(RBS) (Elson and Römer, 2003) metho d is p ossible solutions.
•
Extending driving scenarios: the framew ork presen ted in this dissertation is designed
for high w a y scenario. In city scenario, ho w ev er, the traffic situation is more
complex than on high w a y and th us it is more c hallenging. A dopting v ehicle to
121

7. Discussion and outlo ok
infrastructure (V2I) tec hnology (Djahel, Jab eur, et al., 2015; Djahel, Salehie, et al.,
2013; Barrac hina et al., 2015) as w ell as high precision map (Matthaei, Bagsc hik,
and Maurer, 2014; Seif and Hu, 2016; Sc hreier, Willert, and A dam y, 2013) could
largely reduce the driving uncertain ties in cit y scenario.
•
Ey e-trac king: it has b een pro v ed in man y related researc h that using ey e-trac king
signal has p ositiv e effect on predicting driv er b eha vior (Salvucci and Liu, 2002;
Lethaus and Rata j, 2007; Tijerina et al., 2005; Kaplan et al., 2015). Ho w ev er, the
use of ey e-trac king in this dissertation is limited b y the softw are pro vided b y SMI to
extract features from the ey e-trac k er. T o further extend the usage of ey e-trac king,
computer vision tec hnology is necessary to b e implemen ted (Kim and Ramakrishna,
1999).
•
Driving st yle: Although it is concluded in this dissertation that considering driving
st yle in preparing for training datasets can impro v e the predictiv e p erformance of
driv er LC b eha vior, ho wev er, more researc hes are needed to define driving st yle
as w ell as the role it pla ys in the dev elopment of AD ASs. F or example, based
on F renc h et al. (1993), Glaser and W asc h ulewski (2005), and Vöhringer-Kuhn t
and T rexler-W alde (2005), b y using questionnaire it is capable of defining the
global driving st yle (Sagb erg et al., 2015) of the driv er but the driv er’ temp orary
driving st yles are still needed to b e captured b y going deep in to the driving data.
The temp orary driving st yles can b e represen ted b y the temp orary sp eed c hoice,
accelerating and braking b eha vior, emotional status (stress or fatigue), sensation
seeking and risk taking (Quim b y et al., 1999), as w ell as the attitudes to w ards
sp eed limits (Ahie, Charlton, and Stark ey, 2015). As long as the driv er remains
a part of the con trol lo op, driving and safet y b eha viors are more than just the
mec hanical op eration of a v ehicle (Hennessy, 2011), and th us driving st yle cannot
b e neglected.
122

A
Exp eriment 1
A.1 Do cumen ts
A.1.1 Demographic questionnaire - German v ersion
123

Datum: Pbn.- Nr .:
Im Folgende n werden Ihnen einige Frage n zu Ihrer Per son gestell t.
Ihre Date n werden selbs tverständl ich anonym erhobe n und
ausgewerte t.

1: Geschlecht

weiblich

männlich

2: A lter

In dieses Feld dürfen nur Ziffern eingetragen werden

3: Was ist Ihr höchster Bildungsa b schluss?
Bitte wähle eine der folgenden Ant w orten

Hauptschulabschluss

Mittlere Reife

(Fach-) Abitur

Bachelor / Master / Diplom / Magister

Promotion / Habilitation

4: Besitzen Sie einen Führerschein?

Ja
Nein

5: Seit wie vielen Jahren besitzen Sie eine n Führerschein?

Jahre
In dieses Feld dürfen nur Ziffern eingetragen werden

6 : Wie viele Kilometer sind S ie schon insgesamt seit Erwerb Ihr es
Führerscheins gefahren?

Kilometer
In dieses Feld dürfen nur Ziffern eingetragen werden

7: Wie viele Kilometer fahren S ie im Durch schnitt pro Ja hr?

Kilometer
In dieses Feld dürfen nur Ziffern e i ngetragen werden

8: Haben Sie die Erfahrung mit dem Fahren im Simulator?

Ja
Nein

9: Wie schnell fahren Sie auf der A utobahn bei normalen
Verkehrsbedingungen ?

Km/h
In dieses Feld dürfen nur Ziffern eingetragen werden

10 : Wie oft überholen Sie auf der A utobahn A utos a uf der rechten Spur?
(1 = nicht sehr oft; 5 = sehr oft)

1 2 3 4 5

11 : Wieviel A bstand z um vorausfahrenden Fahrzeug halten Sie beim
Überholvorgang auf der Autobahn?
(1 = wenig A bstand; 5 = v iel Abstand)

1 2 3 4 5

12: Wie fahren Sie im Schnitt bei normalen Verkehrsbedingungen (keine Glätte,
kein Stau etc.)?
(1 = defensiv; 5 = eher flott & zügig)

1 2 3 4 5

Für mö gliche Rüc kfragen möchte ich Sie bitten, Ihre E-Mai ladresse anzug eben:

(Ihre E-M ailadresse w ird natürlich nicht weitergege ben)

Vielen Dan k!

A.1 Do cumen ts
A.1.2 Beha vioral-psyc hological questionnaire - German v er-
sion
Question NO.2, NO.3, NO.8, NO.10, NO.12, NO.16, NO.23 are used for calculating the
score of aggressiv eness, the rest w ould not coun t. The participan ts are not a w are of the
use of this questionnaire.
127

Die f olgende Liste enthäl t kleinere Fehl er u nd Regel übertr etungen, die
Verkehrstei l nehmern von Zei t zu Zeit passieren. Bitt e geben Sie i m folgend en an,
wi e häufig Ihnen diese i m letzten Jah r passi ert sind. Da eine gena ue Angabe of t
schwi erig ist, k reuzen Si e bitte d as Kästchen a n, da s Ihrer Meinung nach am
ehesten zutr iff t.

Nie
(0)

Fast
Nie
(1)

Selten
(2)

Gele-
gentlich
(3)

Häufig
(4)

Sehr
häufig
(5)

1. Sie versuchen, i m fal schen Gang
an der Ampel anzufahren .

2. Sie ärgern sich ü ber ein auf de r
Autobahn links fahrendes
lan gsames Fahrzeug un d
überholen es rechts.

3. Sie fahren dicht auf ein
vorausfahrende s Fahrzeu g auf,
um dem Fahr er zu sign alisieren,
dass er schn eller fahren ode r Ihre
Spur verlassen soll .

4. Sie versuchen, jeman den zu
überholen un d bemerken nicht ,
dass er bereit s nach li nks blinkt
und abbi egen möchte.

5. Sie haben vergess en, wo Sie das
Auto i m Parkhaus oder au f dem
Parkplatz abgestell t haben.

6. Sie betäti gen aus Versehen ein en
Schal ter (z. B. für den Bl inker),
obwohl Si e eigentli ch einen
anderen betäti gen woll ten (z. B.
für die Schei benwischer).

7. Sie stellen fest, das s Sie
eigentli ch nicht genau wi ssen,
wie di e Strecke aussah, die si e
gerade gefahr en sind.

8. Sie fahren noch üb er ein e Ampel,
obwohl Si e wi ssen dass Si e
eigentli ch anhalten mü ssten.

9. Sie bemerken bei m Abbi egen
Fußgänger ni cht, die di e Straße
überqueren.

10. Si e ärgern sich ü ber einen
anderen Fahr er und jag en ihm
hinterher, um i hm zu zei gen, was
Si e vo n i hm halten.

11. Si e erwischen am Kreisverkeh r
di e falsche Ausfahrt.

12. Si e halten si ch nachts oder bei
wenig Verkehr nicht an
Geschwin digkeit sbegre n zung en.

Nie
(0)

Fast
Nie
(1)

Selten
(2)

Gele-
gentlich
(3)

Häufig
(4)

Sehr
häufig
(5)

13. ach ten beim Ei nbi egen in ei ne
Vorfahrtsstra ße so sehr auf den
dortigen Ve rkehr, dass Sie
beinah e auf den Vorderman n auf
Ihrer Spur auffah ren.

15. Su e fahren, obw ohl Sie wi ssen,
dass Sie m öglicherweise mehr
Al kohol getrunken haben als
erlaubt.

16. Si e haben ei ne Abneigung gegen
eine besti mmte Art von
Autofahrern und Sie zei gen ihn en
das, wo imm er Sie könn en.

17. Si e unt ersc hätzen b eim
Überholen di e Geschwi ndigkeit
eines entgegenk ommenden
Fahrzeugs.

18. Si e fahren bei m Zurücksto ßen
gegen etwas, wa s Sie vorh er
ni cht gesehen haben.

19. Si e wollen na ch A fahren und
merken plötz lich, dass Sie sich
auf dem Weg nach B be finden, z.
B. weil Sie so n st imm er nach B
fahren.

20. Si e ordnen si ch vor einer
Kreuzung i n die fal sche Spur ein.

21. Si e übersehen ei n “Vorfahrt
gewähren“ -Sch ild und stoßen
beinah e mit einem
bevorrechti gten
Verkehrsteil nehmer zus ammen.

22. Si e versäumen b eim
Spurwechsel , vor dem
Aussteigen, et c. in den
Rückspi egel zu schauen .

23. Si e l assen sich auf Wettrennen
mi t anderen Autofahrern ein.

24. Si e bremsen auf rut schiger
Fahrbahn zu scharf oder l enken
ni cht r i chtig, so dass si e ins
Schl eudern kommen.

A. Exp erimen t 1
A.1.3 Ov erall instruction on the participan ts - German v ersion
130

Liebe Teilnehmerin, lieber Teilnehmer,
Si e sind eingel aden, an einer Studi e der t echnischen Un iversität Berlin te il zunehmen, di e
sich mi t dem Aut ofahren in e in er Simulation beschäfti gt. Voraussetzu ng en für die
Teilnah me sind , dass Sie den Fü hrerschei n der Kl asse B haben und k eine Bril le tragen .
Wir sichern Ihnen zu, d ass die in dieser Studi e erhobenen Daten ledi glich für
Forschungszw ecke anon ymisi ert ve rwend et und streng ve rtrauli ch behandel t werden.
Rückschlü sse auf die Id entit ät des Ausfüllenden we rden n i cht mögli ch sein.
Die Teil nahme erfolgt frei willig. Si e haben jederzeit das Recht Ihr Ei nverständn is zur
Teilnah me an der Studie, ohn e Angaben von Grün den, zu wi derrufen.
Die Studi e wird ca. 40 Mi nuten dauern und Sie bekomm en für di e Teilnah me an dieser
Studie am End e ein e VP-Stun de oder eine ange messene Aufwan dschädi gung .

Vi e len Dank für Ihre Mi tarbeit un d Ihr Engag ement!

A. Exp erimen t 1
A.1.4 Exp erimen t instruction - German v ersion
132

Sehr geehrte/ r Proband/i n,
vi elen Dank für Ihre Teilnahme an di esem Ex periment. De r Test wi rd im Fahrsi mulator
stattfinden und ungefähr 40 min dauern. Dabei wird mithil f e einer Eye-trac king Bril le die
Blickbew egung mit er fasst.
1. Bitte fül len Sie den F ragebogen (Persönli che Informa tionen zur Fah r erfahr ung) aus!
2. Bitte se tzen Sie sich nun in den Fah rsimulator un d stellen Sie sich den Sit z so ein, dass
Sie bequem die Ped ale und das Lenkrad erreichen können! Der Hebel dafür be f indet
sich unter de m Sitz au f der rechten Seite.
3. Bitte se tzen Sie die Ey e-tracking Bril le erst unter mei ner Anleitun g auf! W ährend der
Kalibrie rung, werde ich S ie bitten an spez i f ische Or te zu schauen.
4. Der vi erte Teil beschäfti gt sich mit den Fa hraufgaben und bit te beachten Sie di e
folgenden Pun k te die ge samte Zeit während Sie fahren!
- Die Fahrau fgaben sind u nterteilt in mehrere klein ere Au fgaben. Sie w erden auf
einer zw e ispurigen Au tobahn f ahren. Die erste F ahrt ist zur Gewöhnung an den
Fahrsimulato r gedacht. Danach be ginnt die eige ntliche Testung.
- Stellen Si e sich während der Fahrt vor, dass sie a uf einer realen Autobahn sind
und fahren Si e Ihren persönl ichen Fahrstil, so w ie Sie gewöhnl ich fahren! Falls
langsamer Verkehr Sie a ufhalten sollte , können S ie die Spur w echseln. Beach ten
Sie dabei den Sicherheit sabstand nach vorn und z ur Seite einzuhal ten!
- Bitte beach ten Sie, dass während der Fahr t die V erkehrsre geln mö glichst
einz uhalten sind, z.B. In nenspiegel, Sei tenspiege l anschauen und rechtzei tiges
Blinken beim Spurwechsel usw.. Hier bitte bea chten Si e, dass Sie den Blinker
richtig runte r rücken und nach Spurw echsel w ieder zurücks tellen müssen.
- Nach dem Übe rholvorgang kehren Sie bi tte wied er auf Ihre ursprün gliche
Fa hrspur zurüc k! Bitte bea chten Sie wei terhin, d ass die Sicherheit aller
Verkehrstei lnehmer gew ährleistet ist , auch zu dem übe rholten Fahrz eug!
- Bitte bew egen Sie w ährend der Fahr t nicht die B rille, da diese ja bereits kalibrie rt
wurde. Ihren Kopf dürfen Sie in kleinen W in k eln drehen, jedoch nicht in größ eren
W in keln ( kein Schulterblick!)!
- Fahren Sie los, sobald das Sz enario startet, und stoppen Sie e rst, w enn auf dem
Bildsch irm „Quit?“ steht !
- Auf dem Au tobahn sind keine Geschwi ndigkeitsbegrenz ungen vorhanden !
Falls Fragen au fkommen , können Si e mich gerne jederzei t fragen!
Vielen Dank für Ihre M itarbeit!

A. Exp erimen t 1
A.2 Figures
A.2.1 Statistics of the demographic questionnaire
1 2
1-Male, 2-Female
0
5
10
15
20 Gender
20 30 40 50 60
Year
0
2
4
6 Age
12345
1-Hauptschulabschluss, 2-Mittlere Reife, 3-Abitur
4-Bachelor/Master/Diplom/Magister, 5-Promotion/Habilitation
0
5
10
15
20 Education
0 5 10 15 20 25 30
Year
0
1
2
3
4 Year of having driving license
02468 1 0
100,000Km
0
10
20
30 Driving total kilometres
0 5 10 15 20 25 30
1,000Km
0
10
20
30 Driving kilometres per year

Figure A.1:
Illustration of the bac kground information of the participan ts in histogram.
134

A.2 Figures
A.2.2 Result of mo del comparison
0 0.5 1
FPR
0
0.5
1
TPR
Aggressive drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Neutral drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Conservative drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Non-categorized
BN with GMM
Naive Bayes
SVM

Figure A.2:
The R OC curves of LCBN-GMM, Naiv e Ba y es, and SVM in Scenario lead
only .
0 0.5 1
FPR
0
0.5
1
TPR
Aggressive drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Neutral drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Conservative drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Non-categorized
BN with GMM
Naive Bayes
SVM

Figure A.3:
The R OC curves of LCBN-GMM, Naiv e Ba y es, and SVM in Scenario lead
+ adjacen t b ehind.
135

A. Exp erimen t 1
0 0.5 1
FPR
0
0.5
1
TPR
Aggressive drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Neutral drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Conservative drivers
BN with GMM
Naive Bayes
SVM
0 0.5 1
FPR
0
0.5
1
TPR
Non-categorized
BN with GMM
Naive Bayes
SVM

Figure A.4:
The R OC curves of LCBN-GMM, Naiv e Ba y es, and SVM in Scenario lead
+2 adjacen t.
136

A.2 Figures
A.2.3 Result of lab eling metho d comparison
0 0.5 1
FPR
0
0.5
1
TPR
Aggressive drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Neutral drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Conservative drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Non-categorized
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window

Figure A.5:
The R OC curv es of using differen t lab eling strategies in Scenario lead only .
0 0.5 1
FPR
0
0.5
1
TPR
Aggressive drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Neutral drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Conservative drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Non-categorized
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window

Figure A.6:
The R OC curves of using differen t lab eling strategies in Scenario lead +
adjacen t b ehind.
137

A. Exp erimen t 1
0 0.5 1
FPR
0
0.5
1
TPR
Aggressive drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Neutral drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Conservative drivers
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window
0 0.5 1
FPR
0
0.5
1
TPR
Non-categorized
Eye-tracking label
5s time window
4s time window
3s time window
2s time window
1s time window

Figure A.7:
The R OC curves of using differen t lab eling strategies in Scenario lead +2
adjacen t .
138

A.3 T ables
A.3 T ables
A.3.1 F ull scale of A UC v alues b y LCBN-GMM using TWL
metho d
T able A.1:
The A UC v alues of LCBN-GMM using TWL metho d in differen t time-windo w
size.
Scenario Driving st yle Length of TW
5 s 4 s 3 s 2 s 1 s
lead only
Aggressiv e 0.88 0.94 0.97 0.97 0.98
Neutral 0.89 0.95 0.97 0.99 0.99
Conserv ative 0.78 0.82 0.82 0.86 0.90
Mean 0.85 0.90 0.92 0.94 0.95
Non-categorized 0.84 0.85 0.93 0.92 0.94
lead + adjacen t b ehind
Aggressiv e 0.87 0.88 0.92 0.95 0.96
Neutral 0.85 0.89 0.91 0.95 0.97
Conserv ative 0.74 0.77 0.80 0.84 0.96
Mean 0.82 0.85 0.87 0.91 0.96
Non-categorized 0.81 0.84 0.88 0.91 0.92
lead + 2 adjacen t
Aggressiv e 0.80 0.83 0.88 0.91 0.92
Neutral 0.69 0.75 0.73 0.75 0.80
Conserv ative 0.68 0.73 0.77 0.81 0.90
Mean 0.72 0.80 0.78 0.82 0.87
Non-categorized 0.69 0.77 0.70 0.79 0.80
139

B
Big data analysis
B.1 T ables
B.1.1 Result of feature selection for LLC scenarios
T able B.1: The full scale effect size of the features in LLC scenarios.
#
LLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
1 0.75 0.10 0.75 0.10 0.81 0.05 0.66 0.14
2 0.81 0.09 0.71 0.13 0.74 0.13 0.79 0.10
3 0.06 0.78 0.07 0.75 0.04 0.86 0.07 0.75
4 – – 0.92 ▲ 0.04 ▼ – – 1.18 ▲ 0.01 ▼
5 0.51 0.18 0.59 0.11 0.31 0.27 0.60 0.16
6 1.02 ▲ 0.03 ▼ 0.98 0.05 0.98 0.02 1.54 ▲ 0.04 ▼
7 0.97 0.06 0.98 ▲ 0.04 ▼ 1.14 ▲ < 0.01 ▲ 1.32 0.07
8 0.91 0.06 0.89 0.06 1.00 0.01 0.83 0.07
9 0.86 0.06 0.88 0.06 0.89 0.06 0.92 0.09
10 0.82 0.07 0.79 0.09 0.86 0.10 1.04 0.10
11 0.98 0.04 1.05 ▲ 0.03 ▼ 0.91 0.05 0.98 0.08
12 0.97 0.04 1.04 0.05 1.21 0.12 1.03 ▲ 0.01 ▼
13 0.99 ▲ 0.04 ▼ 0.97 0.06 1.08 0.06 0.79 0.02
14 0.91 0.06 0.90 0.08 0.77 0.03 1.07 0.09
15 0.80 0.09 0.73 0.12 0.63 0.17 0.84 0.11
16 1.00 ▲ 0.04 ▼ 0.93 0.04 0.62 0.08 1.34 0.02
17 0.94 0.05 0.97 ▲ 0.03 ▼ 0.94 0.08 1.36 ▲ 0.03 ▼
Con tinued on next page
141

B. Big data analysis
T able B.1 – Con tin ued f rom previous page
#
LLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
18 0.98 0.06 0.93 0.04 0.78 0.11 1.40 0.06
19 0.92 0.07 0.88 0.06 0.74 0.07 1.23 0.09
20 0.84 0.06 0.84 0.08 0.79 0.10 0.91 0.05
21 0.98 0.04 0.95 ▲ 0.03 ▼ 0.89 0.06 1.38 0.07
22 1.03 ▲ 0.03 ▼ 0.94 0.04 0.77 0.14 1.20 ▲ 0.04 ▼
23 1.00 0.04 0.94 0.05 1.04 ▲ 0.03 ▼ 0.72 0.05
24 0.94 0.05 0.91 0.07 0.91 0.02 0.56 0.01
25 0.88 0.07 0.82 0.07 0.90 0.09 0.99 0.05
26 0.92 ▲ 0.04 ▼ 0.95 0.05 1.11 0.06 1.18 0.08
27 0.91 0.06 0.93 0.05 0.95 ▲ < 0.01 ▼ 1.15 0.10
28 0.87 0.06 0.83 0.06 0.80 0.03 1.23 0.07
29 0.80 0.09 0.82 0.06 0.87 0.06 0.98 0.06
30 0.79 0.08 0.77 0.09 0.82 0.09 1.01 0.09
31 0.92 ▲ 0.04 0.94 0.04 0.87 ▲ 0.01 ▼ 0.70 0.02
32 0.91 0.03 ▼ 0.95 ▲ 0.04 ▼ 0.67 0.03 0.59 0.02
33 0.87 0.04 0.86 0.06 0.59 0.11 0.85 0.07
34 0.86 0.07 0.85 0.06 0.66 0.13 0.80 0.07
35 0.83 0.07 0.81 0.10 0.75 0.06 0.71 0.10
36 0.91 ▲ 0.04 ▼ 1.05 ▲ 0.04 ▼ 1.06 0.01 1.00 0.05
37 0.93 0.05 0.98 0.05 1.07 0.01 1.05 0.09
38 0.99 0.05 0.93 0.07 1.23 ▲ < 0.01 ▼ 0.92 0.10
39 0.94 0.07 0.88 0.08 1.10 < 0.01 0.74 0.06
40 0.75 0.10 0.75 0.11 0.87 0.01 0.54 0.08
41 0.98 ▲ 0.04 ▼ 0.99 0.04 0.76 0.08 0.73 0.03
42 0.96 0.04 1.01 ▲ 0.03 ▼ 0.78 0.06 0.53 < 0.01
43 0.90 0.05 0.95 0.05 0.85 0.04 0.79 ▲ 0.01 ▼
44 0.89 0.06 0.91 0.04 0.71 0.03 0.60 0.05
45 0.87 0.07 0.86 0.09 0.49 0.08 0.65 0.05
46 0.93 ▲ 0.03 ▼ 0.90 ▲ 0.04 ▼ 0.58 0.14 0.90 0.01
47 0.89 0.04 0.88 0.05 0.66 0.11 0.82 < 0.01
48 0.85 0.06 0.87 0.06 0.74 0.17 1.00 < 0.01
49 0.81 0.07 0.91 0.06 0.45 0.07 0.89 0.04
50 0.84 0.07 0.84 0.08 0.63 0.16 1.04 ▲ 0.03 ▼
51 0.88 ▲ 0.04 ▼ 0.94 ▲ 0.04 ▼ 0.97 ▲ 0.01 ▼ 0.94 0.05
52 0.86 0.05 0.91 0.05 0.61 0.04 0.83 < 0.01
53 0.81 0.05 0.88 0.06 0.61 0.15 0.82 < 0.01
54 0.82 0.07 0.85 0.06 0.74 0.05 0.91 ▲ 0.03 ▼
Con tinued on next page
142

B.1 T ables
T able B.1 – Con tin ued from previous page
#
LLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
55 0.84 0.07 0.81 0.10 0.79 0.05 0.83 0.05
56 0.37 0.34 0.44 0.28 0.31 0.38 0.30 0.39
57 0.36 0.34 0.44 0.27 0.33 0.37 0.39 0.31
58 0.35 0.33 0.44 0.25 0.30 0.40 0.34 0.25
59 0.31 0.35 0.43 0.23 0.28 0.48 0.38 0.19
60 0.24 0.40 0.34 0.27 0.23 0.48 0.39 0.25
61 0.91 0.05 0.96 0.05 1.21 ▲ < 0.01 ▼ 0.76 0.06
62 0.90 0.05 0.95 0.05 1.16 0.01 1.02 ▲ 0.03 ▼
63 0.93 0.07 0.93 0.05 1.06 0.07 0.93 0.03
64 0.91 0.07 0.85 0.08 1.02 0.04 0.89 0.07
65 0.73 0.08 0.69 0.12 0.82 0.01 0.70 0.05
66 0.88 0.06 0.91 0.06 1.00 < 0.01 0.79 0.06
67 0.87 0.07 0.90 0.06 0.97 0.07 0.77 0.02
68 0.91 0.07 0.89 0.07 1.06 ▲ 0.04 ▼ 0.70 0.04
69 0.87 0.07 0.82 0.10 0.98 0.05 0.63 0.02
70 0.70 0.08 0.67 0.12 0.70 0.03 0.59 0.06
71 0.94 ▲ 0.04 ▼ 0.94 0.06 0.98 < 0.01 0.95 0.06
72 0.93 0.05 0.94 0.06 0.99 ▲ < 0.01 ▼ 1.12 0.05
73 0.94 0.06 0.91 0.07 1.03 0.11 0.98 0.06
74 0.91 0.07 0.82 0.08 0.98 0.07 0.98 0.07
75 0.71 0.10 0.65 0.11 0.81 0.05 0.80 0.05
76 0.83 0.06 0.84 0.07 0.71 0.07 0.64 0.04
77 0.82 0.07 0.81 0.08 0.76 0.03 0.65 0.08
78 0.79 0.09 0.80 0.11 0.62 0.04 0.59 0.07
79 0.67 0.11 0.64 0.13 0.59 0.18 0.52 0.09
80 0.48 0.20 0.44 0.21 0.47 0.16 0.42 0.13
81 0.91 0.05 0.96 0.05 0.81 0.02 0.75 0.06
82 0.94 0.05 0.83 0.07 0.87 ▲ 0.01 ▼ 1.20 0.08
83 0.90 0.07 0.85 0.09 0.70 0.07 1.14 ▲ 0.04 ▼
84 0.84 0.09 0.78 0.11 0.59 0.12 0.90 0.02
85 0.69 0.14 0.62 0.15 0.83 0.10 0.73 0.13
86 0.98 ▲ 0.04 ▼ 0.97 0.06 0.61 0.07 0.83 ▲ 0.02 ▼
87 0.86 0.06 0.89 0.06 0.79 0.08 0.86 0.07
88 0.89 0.07 0.87 0.07 1.03 0.05 0.66 0.06
89 0.82 0.09 0.79 0.10 0.87 0.07 0.83 0.10
90 0.66 0.14 0.59 0.17 0.74 0.10 0.72 0.13
91 0.92 0.05 0.84 0.05 0.97 < 0.01 1.16 0.10
92 0.87 0.05 0.79 0.09 1.03 0.08 0.90 0.14
Con tinued on next page
143

B. Big data analysis
T able B.1 – Con tin ued f rom previous page
#
LLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
93 0.81 0.09 0.81 0.10 1.18 ▲ < 0.01 ▼ 0.65 0.16
94 0.79 0.09 0.80 0.09 1.09 0.11 0.74 0.09
95 0.65 0.15 0.61 0.17 1.01 0.11 0.52 0.11
144

B.1 T ables
B.1.2 Result of feature selection for RLC scenarios
T able B.2: The full scale effect size of the features in RLC scenarios.
#
RLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
1 0.66 0.11 0.60 0.12 0.76 0.09 0.66 0.19
2 0.69 0.12 0.96 ▲ 0.04 ▼ 0.81 0.09 0.79 0.08
3 0.06 0.78 0.07 0.72 0.07 0.76 0.07 0.74
4 – – – – 0.84 0.06 1.18 ▲ < 0.01 ▼
5 0.55 0.16 0.82 ▲ 0.02 ▼ 0.62 0.13 0.71 0.04
6 0.92 ▲ 0.04 ▼ 1.29 ▲ 0.04 ▼ 0.96 ▲ 0.04 ▼ 1.54 ▲ < 0.01 ▼
7 0.91 0.04 0.01 0.11 0.94 0.06 1.32 0.02
8 0.92 0.05 0.72 0.03 0.85 0.07 0.83 0.01
9 0.89 0.07 1.11 < 0.01 0.80 0.09 0.92 0.01
10 0.78 0.08 0.94 0.14 0.78 0.08 1.04 0.08
11 1.01 ▲ 0.03 ▼ 1.29 0.02 1.02 ▲ 0.03 ▼ 0.98 ▲ 0.03 ▼
12 0.95 0.04 1.28 < 0.01 0.99 0.03 1.03 0.11
13 0.96 0.05 1.60 ▲ < 0.01 ▼ 0.97 0.07 0.79 0.02
14 0.91 0.09 1.34 < 0.01 0.88 0.09 1.07 0.10
15 0.79 0.10 1.02 0.03 0.74 0.12 0.84 0.09
16 0.97 ▲ 0.03 ▼ 1.28 < 0.01 0.95 ▲ 0.04 ▼ 1.34 < 0.01
17 1.02 0.05 1.13 < 0.01 0.97 0.05 1.36 < 0.01
18 0.99 0.05 1.03 < 0.01 0.96 0.05 1.40 ▲ < 0.01 ▼
19 0.93 0.06 1.38 < 0.01 0.91 0.06 1.23 < 0.01
20 0.88 0.06 0.93 < 0.01 0.83 0.06 0.91 0.05
21 0.98 ▲ 0.04 ▼ 1.10 ▲ 0.02 ▼ 1.06 ▲ 0.02 ▼ 1.38 ▲ < 0.01 ▼
22 0.93 0.04 0.98 < 0.01 1.03 0.04 1.20 0.03
23 0.93 0.04 0.60 0.11 0.94 0.06 0.72 0.04
24 0.89 0.07 0.24 0.23 0.91 0.06 0.56 0.09
25 0.80 0.08 0.69 0.04 0.82 0.07 0.99 0.14
26 0.90 0.05 0.65 0.06 1.04 ▲ 0.03 ▼ 1.18 < 0.01
27 0.92 0.05 0.83 0.06 1.00 0.04 1.15 0.03
28 0.89 0.05 1.05 0.11 0.85 0.07 1.23 ▲ 0.01 ▼
29 0.77 0.08 0.90 0.16 0.77 0.08 1.01 0.07
31 0.87 0.06 1.16 ▲ < 0.01 ▼ 0.96 0.04 0.70 0.02
32 0.89 0.06 0.88 < 0.01 0.98 ▲ 0.04 ▼ 0.59 0.05
33 0.89 0.07 0.69 0.02 0.95 0.03 0.85 0.06
34 0.87 0.06 1.04 0.07 0.95 0.06 0.80 0.06
35 0.83 0.06 1.07 0.09 0.83 0.07 0.71 < 0.01
36 0.91 ▲ 0.04 ▼ 0.85 ▲ 0.02 ▼ 0.94 ▲ 0.04 ▼ 1.00 0.06
Con tinued on next page
145

B. Big data analysis
T able B.2 – Con tin ued f rom previous page
#
RLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
37 0.96 0.05 0.66 0.03 0.92 0.05 1.05 0.06
38 0.97 0.05 0.79 0.05 0.90 0.07 0.92 ▲ 0.03 ▼
39 0.90 0.06 0.81 0.21 0.84 0.11 0.74 0.05
40 0.71 0.12 0.89 0.11 0.69 0.13 0.54 0.22
41 0.92 0.04 1.00 0.10 0.98 ▲ 0.04 ▼ 0.73 0.07
42 0.95 ▲ 0.04 ▼ 1.12 0.16 0.87 0.04 0.53 0.09
43 0.93 0.05 0.90 0.09 0.90 0.04 0.79 0.11
44 0.93 0.04 0.63 0.06 0.84 0.06 0.60 0.13
45 0.91 0.05 0.90 0.02 0.83 0.07 0.65 0.01
46 0.84 ▲ 0.04 ▼ 1.28 ▲ 0.02 ▼ 0.94 ▲ 0.03 ▼ 0.90 0.06
47 0.85 0.05 1.30 0.05 0.92 0.03 0.82 0.08
48 0.83 0.05 1.01 0.09 0.95 0.05 1.00 0.04
49 0.87 0.07 0.90 0.02 0.95 0.05 0.89 0.05
50 0.80 0.09 0.89 0.05 0.90 0.08 1.04 0.01
51 0.91 0.05 1.01 ▲ < 0.01 ▼ 0.90 0.05 0.94 ▲ < 0.01 ▼
52 0.88 0.05 0.63 0.09 1.00 ▲ 0.04 ▼ 0.83 0.06
53 0.85 0.07 0.54 0.02 0.93 0.04 0.82 0.10
54 0.84 0.07 0.92 0.09 0.93 0.08 0.91 0.04
55 0.82 0.06 1.07 0.06 0.83 0.07 0.83 0.01
56 0.41 0.31 0.58 0.12 0.48 0.26 0.30 0.46
57 0.42 0.30 0.54 0.10 0.48 0.26 0.39 0.24
58 0.39 0.31 0.71 0.09 0.47 0.25 0.34 0.23
59 0.36 0.30 0.58 0.13 0.43 0.26 0.38 0.17
60 0.28 0.35 0.38 0.27 0.32 0.30 0.39 0.12
61 0.96 0.05 1.16 ▲ 0.02 ▼ 0.95 0.06 0.76 0.03
62 0.93 0.07 1.11 0.10 0.91 0.07 1.02 ▲ 0.01 ▼
63 0.97 0.08 0.96 0.09 0.86 0.07 0.93 0.01
64 0.90 0.05 0.80 0.04 0.79 0.09 0.89 0.07
65 0.72 0.09 0.79 0.03 0.70 0.09 0.70 0.17
66 0.92 0.07 1.13 ▲ 0.01 ▼ 0.97 0.05 0.79 0.01
67 0.91 0.06 0.86 0.15 0.93 0.07 0.77 0.07
68 0.90 0.08 0.96 0.08 0.88 0.07 0.70 0.18
69 0.84 0.09 0.86 0.05 0.79 0.10 0.63 0.17
70 0.65 0.11 0.67 0.17 0.65 0.13 0.59 0.24
71 0.94 0.06 1.10 ▲ 0.02 ▼ 0.85 0.08 0.95 0.13
72 0.92 0.05 1.09 0.01 0.92 0.08 1.12 ▲ 0.03 ▼
73 0.95 0.07 0.88 0.11 0.89 0.08 0.98 0.01
74 0.86 0.07 0.76 0.05 0.82 0.09 0.98 0.02
Con tinued on next page
146

B.1 T ables
T able B.2 – Con tin ued from previous page
#
RLC Scenario
0_0 0_1 1_0 1_1
d p d p d p d p
75 0.70 0.10 0.72 0.15 0.68 0.10 0.80 0.11
76 0.77 0.08 1.06 ▲ 0.04 ▼ 0.77 0.10 0.64 0.15
77 0.76 0.10 1.08 0.06 0.78 0.11 0.65 0.17
78 0.72 0.13 0.91 0.02 0.74 0.12 0.59 0.13
79 0.67 0.14 0.80 0.10 0.69 0.14 0.52 0.24
80 0.44 0.20 0.53 0.12 0.49 0.23 0.42 0.20
81 0.86 0.06 0.95 ▲ 0.01 ▼ 0.93 0.05 0.75 0.08
82 0.88 ▲ 0.04 ▼ 0.43 0.01 0.90 0.06 1.20 ▲ 0.03 ▼
83 0.87 0.08 1.36 0.05 0.82 0.08 1.14 0.01
84 0.83 0.11 0.88 0.03 0.78 0.10 0.90 0.09
85 0.68 0.15 0.62 0.05 0.68 0.14 0.73 0.06
86 0.90 0.06 0.88 ▲ 0.02 ▼ 0.95 ▲ 0.04 ▼ 0.83 0.09
87 0.88 0.08 0.86 0.14 0.88 0.07 0.86 ▲ 0.04 ▼
88 0.81 0.08 0.70 0.12 0.87 0.08 0.66 0.09
89 0.72 0.13 0.63 0.23 0.75 0.10 0.83 0.13
90 0.58 0.19 0.57 0.10 0.59 0.17 0.72 0.16
91 0.89 0.07 0.52 0.01 0.81 0.07 1.16 ▲ 0.02 ▼
92 0.88 0.07 0.88 0.10 0.88 0.06 0.90 0.09
93 0.90 0.07 1.05 0.02 0.86 0.06 0.65 0.14
94 0.79 0.08 1.11 ▲ < 0.01 ▼ 0.82 0.09 0.74 0.11
95 0.59 0.15 1.09 < 0.01 0.61 0.15 0.52 0.18
147

B. Big data analysis
B.2 Figures
B.2.1 Result of mo del p erformance using selected features for
LLC scenarios
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Naive Bayes
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
KNN
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
SVM
Selected
All Feautres
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Decision Tree
Selected
All Features

Figure B.1: The R OC curv es of the classification p erformance for LLC Scenario 0_0.
148

B.2 Figures
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
SVM
Selected
All Features
0 0.5 1
FPR
0
0.5
1
TPR
Naive Bayes
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Decision Tree
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
KNN
Selected
All Features

Figure B.2: The R OC curv es of the classification p erformance for LLC Scenario 0_1.
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
SVM
Selected
All Features
0 0.5 1
FPR
0
0.5
1
TPR
Naive Bayes
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Decision Tree
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
KNN
Selected
All Features

Figure B.3: The R OC curv es of the classification p erformance for LLC Scenario 1_0.
149

B. Big data analysis
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
SVM
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Naive Bayes
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Decision Tree
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
KNN
Selected
All Features

Figure B.4: The R OC curv es of the classification p erformance for LLC Scenario 1_1.
150

B.2 Figures
B.2.2 Result of mo del p erformance using selected features for
RLC scenarios
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
SVM
Selected
All Features
0 0.5 1
FPR
0
0.5
1
TPR
Naive Bayes
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Decision Tree
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
KNN
Selected
All Features

Figure B.5: The R OC curv es of the classification p erformance for RLC Scenario 0_0.
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
SVM
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Naive Bayes
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Decision Tree
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
KNN
Selected
All Features

Figure B.6: The R OC curv es of classification p erformance for RLC Scenario 0_1.
151

B. Big data analysis
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
SVM
Selected
All Features
0 0.5 1
FPR
0
0.5
1
TPR
Naive Bayes
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Decision Tree
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
KNN
Selected
All Features

Figure B.7: The R OC curv es of the classification p erformance for RLC Scenario 1_0.
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
SVM
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Naive Bayes
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
Decision Tree
Selected
All Features
0 0.2 0.4 0.6 0.8
FPR
0.2
0.4
0.6
0.8
1
TPR
KNN
Selected
All Features

Figure B.8: The R OC curv es of the classification p erformance for RLC Scenario 1_1.
152

C
Exp eriment 2
C.1 Do cumen ts
C.1.1 Driv er selection questionnaire - German v ersion
Question NO.4, NO.6, NO.7, NO.8, NO.9, NO.10 are used for selecting participan ts.
153

E CHT F AHRT -S TUDIE
Screener: Driver Intention II Name:__ __________ ________
1.) W ie alt sind Sie ? _________
2 .) W el che Führersc heinklassen b esitzen Sie? __________
3.) W ie lange besitzen Sie Ihr en Führersc hein bereits ? __________
4. ) Habe n Sie Problem e beim Hören?
W enn ja  Ende ○ Ja, ○ Nein
6 .) Habe n Sie Schwieri gkeiten Ihre Hä nde und/oder F inger zu bewegen?
W enn ja  Ende ○ Ja, ○ Nein
7 .) Habe n Sie Problem e in der Motorik i m rechten Fuß?
W enn ja  Ende ○ Ja, ○ Nein
8 .) Nehm en Sie regelm äßig Medik am ente ein, die Ihre Fa hrleistung be einträchtigen k önnen ?
W enn ja  Ende ○ Ja, ○ Nein
9 .) Habe n Sie eine Farbse hsch wäche?
W enn ja  Ende ○ Ja, ○ Nein
10 .) Tragen Sie eine Brille? ○ Ja, ○ Nein
 W enn Brille: Besitzen Sie auch Kontak tlinsen, die Sie zum Versuch trage n können? Dies ist
wichtig, da wir im Versuch I hre Blick bewegung m it Eye -Track ing mess en und eine Brille die
Messung stör en kann.
W enn Fahrt n ur m it Brille möglich  E nde
11 .) Bitte erscheinen Sie zu dem Versuch ohne Augen -M ak e- up, oder stell en Sie s ich darauf ein,
dieses v or Or t zu entfernen . Sind Sie hierm it einversta nden?
W enn nei n  Ende ○ Ja, ○ Nein
12 .) Bi tte erscheinen Sie z um Versuch in einem gesundh eitlich guten Zusta nd und s ein Sie zum
Zeitpunk t der Versuchsdurc hführung f ahrtüchtig. S ind Sie hierm it ei nverstan den?“
W enn nei n  Ende ○ Ja, ○ Nein
13.) Bitte br ingen Sie zum Versuch ihren Fü hrersc hein und Person alaus wei s m it. Sin d Sie dam it
einverstanden?
W enn nei n  Ende ○ Ja, ○ Nein
Uhrzeit und Dat um des v ereinbarten T ermins: _____ ______ ___________ ___________ _______
Handynumme r für Notfälle:___ ____________ _______________ ___________ ________ ________

C.1 Do cumen ts
C.1.2 Recruiting participan ts - German v ersion
155

Er eignisprotok oll

HMI & P RODUCT S TRATEGY

last change
Jan 2019

Revision:
0.1

Page
1 of 2

© 2013 All rights rese rv ed b y TAKAT A AG. Document file: 00_ Ereignisprot okoll

PB -Nr.:____________ _ __
Datum__ __________ ___
Uhrzei t______________

Besonde re Ereignisse :

Uhrzei t

W as ?

Strecke (Abschni t t 1,2 o der 3)

Verbale Ermahn ung de s Probanden w ar not w endig in Fahr t…nach …min… w egen :
Fahrt

Minute

Grund des verbal en Ein grei f ens

Fehlende Daten (z.B. fe hlende Videoau fzeichn ung), techni sche Sch w ierigkeiten:
________________________________________ ________________________________________ _
________________________________________ ________________________________________ _
________________________________________ ________________________________________ _
________________________________________ ________________________________________ _

Page 2 of 2

HMI & P RODUCT S T RATEGY

Sonstiges:
________________________________________ ________________________________________ _
________________________________________ ________________________________________ _
________________________________________ ________________________________________ _
________________________________________ ________________________________________ _
________________________________________ ________________________________________ _

Sitzposit ion:
Tit el

M aß

W ink el d. Rückenlehne

W ink el d. Lenkrades

Höhe d. Sitzes (cm)

Abstand d. Sitzes (cm)

Abstand d. Lenkrades (cm)

C. Exp erimen t 2
C.1.3 Demographic questionnaire - The graphical user in ter-
face in German
158

C. Exp erimen t 2
C.1.4 Exp erimen tal instruction - German v ersion
160

Vorbereitun g (Am Auto)
1. Rekrutierun g
 Mit Probanden Sc reener du rchgehen
 Probanden einladen (Kalende reintrag)
2 . Auto starten:
 T ür ö ffnen
 Innerhalb von 1 Mi n. Auto st a rten
 Lenkrad für 2 M in. nicht anfassen
 Auf das blinkende grüne Licht in der Mi ttelkonsole achten
3. Laptop s tarten und Ey etracker an schließen :
 In Versuche  „Driv er Intention Studie“  Ko dierung des Probanden :
VP_Nr_A bschnitt (z.B. VP_1_1, VP_1 _2, VP_1_ 3)
4. Kalibrierun gsmatrix auf den Bei f ahrersitz l egen.
5. Ereignispro tokoll (2x) und Einv erständniser klärung (1x) ausd rucken
Begrüßung: (I m E-Labo r)
Herzl ich W illkommen z u unserer Echtfahrzeu g -Stud ie!
Zusammenfassun g der Studie
(Im E- Labo r)
Bev or wir beginnen, fasse ich Ihnen zun ächst einmal die wichtigsten Information en der
heutigen Studie zusa mmen. Bei w eiteren Fragen können Sie sich selbstverständli ch
jederzeit an mich w enden.
Zw eck der Studie ist die Untersuchun g des Fahrv erhaltens unter Berüc ksichtigung der
Handposi tion, der Au genbew egung und verschied ener Fah rdaten in einer real en
Fahrsituation .
Es handelt sich bei diesem Fahrzeu g um ein Erprobungsfahrz eug m it Automati kgetriebe ,
das s mit einer spezi ellen M esstechnik aus ges tattet ist. In dem Lenkrad be f inden sich
kapazitiv e Sensoren mit denen wir Ihre Handpo sition erf assen . Zusätz lich werden I hre
Blickbew egungen mit Hilfe eines Eyetrackers aufgenommen . Dieser muss vor Beginn der
Fahrt k alibriert werden. Geg eben enfalls muss die Kalibrierung im Verlau f der Studie
wiederhol t w erden. Außerdem werden v erschiedene Fahrmanöver, wie z .B. Abbiegen ,
Überholen und Kurven f a hren, durch meinen Kollegen kodiert.
Haben Si e bis hierhin no ch Fragen?

Diese Studi e wird insges amt etwa 3 S tunden dau ern und beinh altet zw e i Pausen. W i r fahren
darin eine f est gelegte St recke.
 Laminierte Strec ke zeigen
Sie müssen sich den W e g nicht me rken. Diese A bbildu ng dient lediglich d er Orientier ung.
W äh rend der Studie werde ich Ihnen stets rechtsz eitig mitteil en, in wel che Richtun g Sie
weiter f ahren müssen (Si ehe Kommenta r). Sofern Sie keine w eiteren Instr uktionen erhal ten,
setzen Si e die Strec ke in Fahrtrichtun g fort.
Kommentar :
 Die Änd erung der Fahrtri chtung soll de m Proband en rechtsz eitig mitgeteilt werden ,
dh. die Ins truktion erfolgt mindes t en 10 Seku nden bevor die Fahrtrich tung geän dert
wird. In die spä tere Unte rsuchung gehen Daten von 4 Sekunden vor dem
Fahrmanöve r mit ein. Zie l ist es ein möglichst nat ürliches Fahrve rhalten z u erreichen.
 Es soll bei Instruktionen vermieden werden Land marken z u wählen, die
unmittelba ren Einfluss a uf das Bli ckverhalten ha ben (beispi elsweise Stra ßennamen ,
die können, wenn sie nic ht bekannt si nd, erst aus der Nähe identifiz iert w erden) .
Besser ist es rechtz eitig große Gebäude/Ampel n, die bereit s aus der Fern e z u sehen
sind z u nennen. Alternati v soll auf Richtungsa ngabe n (beispielsweise an der
kommenden St raße rech ts) z urückgegriffen werd en.
 Fall s es nicht möglich de m Probanden mindesten s 10 Sekunden vo r Fahr manöver
einen Fahrtri chtungswec hsel mitz uteilen, werden Instruktion gebündelt mit geteilt
(beispiel sweise an der kommenden Kreuz ung rechts und danach direk t wieder links
abbiegen).
 Sobal d ein Fahrmanöver erfolgt is t, soll dem Prob anden direkt angekündig t werden,
wann er das nächs t e M al die Fahr trichtung ge wechselt wird (beispiel sweise folgen
Sie dem S traßenverlauf für 3 km).
Haben Si e dazu Fra gen? W enn nei n, dann fort fahren.
Fragebo gen und Einv erständniserklärun g
Bev or wir di e Fahrt beginnen, w ürde ich Si e bitte die Einv erständnis erklärun g genau zu
lesen und z u untersch reiben .
Toilette
Sie haben jetzt noch mal die M öglichkeit au f di e T oilette zu g ehen, wenn Sie möchten.
Später w erden Sie in den Pausen eben f alls die Möglichkeit haben die Toilette zu benutzen,
ansonsten w ürde ich un gerne die S tudie unterbre chen.

Instruktionen am Erp robungsfahrz eug
Sie können sich nun in das Fahrzeu g setzen und den Fahrersitz sowie die Spiegel in eine
geeignete Posi tion einst ellen. Fall s Sie Hilfe ben ötigen , hel fe ich Ihnen gerne w eiter.
 Lüftung des Fah rzeugs au f maximal stell en
Dann z eige ich Ihnen zunäch st einmal die Bed ie nung des Fahrz eugs.

Bedienun g des Fahrzeu gs :
Erläuterun g wie:
1. das Fahrzeug gestartet und gestoppt wi rd
 „Das Fahrzeu g ist be reits gestarte t. Stoppen Si e das Fahrz eug nur, wenn wi r Sie
explizi t drauf hinw eisen.“
 „Sie können bei Bedar f das Fahrzeu g parken, ind em Sie den P -Knop f drücken . Bitte
schalten Sie das Fahrze ug dabe i nicht aus.“
2. die Handbremse und Gänge eingelegt w erden

Assistenz systeme:
1. Head- up -Display
Haben Si e zur Bedienun g des Fahrzeu gs noch Fragen? W e nn nein, dann fort fahren.
Sie können z u jedem Zei tpunkt den Versuch abbrechen . Möchten Sie den Versuch
ab brechen, können Sie d ies dem Versuchsl eiter ei nf ach mi tteilen. W ir hal ten dann an der
nächsten Gele genheit an und bringen Sie zurüc k zu Joy son Safety Sy stems. Andere rseits
kann durch den roten No tabschaltun gsknopf in de r Mittelkonsole die M esstechnik auch
direkt ab schaltet werden. Achten Sie bi tte darauf, dass der Knop f nicht v ersehentlich,
sondern nur i m Notfall gedrückt w ird, da Ihre Daten sonst verloren gehen .
Selbstv erständlich müssen Sie sich an die Straßenverkehrsordnun g halten. Achten Sie
besonders da rauf, dass wenn Sie auf der Aut obahn Fahrzeuge überho len, Sie sich im
Anschluss w ieder rechts einsortieren .
Haben Si e dazu noch Fragen?

Kalibrie rung des Eyetrac kers
W ir werden den Eyetrac ker mindesten dreimal im Ve rlau f der S tudie kalibrie ren.
Gegebenen falls muss de r Eyetrac ker inne rhalb eines Studienab schnittes außerpl anmäßig
nachkalibrier t werden. Is t dies der Fal l, werde ich Sie au ff ordern bei der nä chsten
Gelegenhei t rechts anzuh alten, damit w ir die Kalibrierun g wiederhol en könn en.
Dann beginnen wi r jetzt mit der ers ten Kalibrieru ng.

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