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Cognition, T echnology & W ork (2020) 22:733–744
https://doi.org/10.1007/s10111-020-00626-z
ORIGINAL ARTICLE
T ake ‑ ov er expectation andcriticality inLev el 3 automa ted driving:
atest tr ack study ontake ‑ ov er behavior insemi‑trucks
AlexanderL otz 1,2 · NeleRusswinkel 2 · Enrico W ohlfar th 1
Received: 27 No vember 2019 / Ac cepted: 21 F ebruar y 2020 / Published online: 4 Mar ch 2020
© The Author(s) 2020
Abstr ac t
With the introduction of adv anced dr iving assistance sys tems managing longitudinal and lateral control, conditional auto -
mated dr iving is seemingl y in near future of ser ies v ehicles. While take-o v er beha vior in the passenger car conte xt has
been in v estig ated intensivel y in recent years, publications on semi-trucks with prof essional dr iv ers are sparse. The effects
influencing e xper t dr iv ers dur ing take-o v ers in this context lac k t horough in ves tigation and are req uired to design sy stems
that f acilit ate saf e take-o v ers. While multiple findings seem to cohere in passenger cars and semi-trucks, these findings rel y
on simulated studies without taking envir onments as f ound in the real wor ld into account. A test trac k study w as conducted,
simulating highw a y dr iving with 27 prof essional non-affiliated tr uck drivers. The participants dro v e an automated Lev el 3
semi-tr uc k while a non-dr iving-related task w as a v ailable. Multiple time cr itical take-o v er situations were initiated during
the dr iv es to in v estigate f our main objectiv es regarding driver beha vior . (1) With these results, compar ison of reaction times
and beha vior can be dra wn to previous simulator s tudies. The effect of situation cr iticality (2) and training (3) of take-o v er
situations is in v estig ated. (4) The influence of w ar ning e xpect ation on driver beha vior is e xplored. Results obtained displa y ed
v er y quic k time to hands on steer ing and time to first reaction all under 2.4 s. Highl y cr itical situations generate v er y quic k
reaction times M = 0.81 s, while the manipulation of expectancy yielded no significant v ar iation in reaction times. These
reaction times ser v e as a ref erence of what can be expected from driv ers under optimal t ak e-ov er conditions, wit h q uick
reactions at high speed in critical situations.
Keywords Conditional aut omated dr iving· T r uck driv ers· Dr iv er take-o ver· T es t-track s tudy· W arning expectancy
1 Introduction
The ter m ‘automated driving’ has become a global discus -
sion point in recent y ears, with political and social inter -
ests rising to dev elop vehicles enabling automation. While
the progress has been significant within t he past decade,
complete automation of the driving t ask has not y et been
achie ved. Of late, ne w assistance sys tems are enter ing
the marke t to suppor t dr iv ers in restricted scenar ios such
as Audi’ s tr affic jam pilot (Audi 2017 ), T esla ’ s Autopilo t
(T esla 2019 ) and the Mercedes Activ e Dr iv e Assist f or
tr uc ks (Daimler 2018 ). These systems rank as Le vel 2 par tial
automation and Le v el3 conditional automated driving (S AE
J 3016 2018 ). Especiall y f or Lev el3, the difficulty of transi -
tions betw een mac hine and dr iv er is an ongoing researc h
field, in which the sy stem reac hes limits and the dr iver needs
to regain contr ol of the vehicle in short timeframes (Y oung
etal. 2007 ). Researc h within the field of automation tran -
sitions has sho wn that human capabilities can deter iorate
due to task switching (Bainbridge 1983 ; W y lie etal. 2000 )
and a lac k of situation a w areness (Endsle y 1995 ; Y oung
et al. 2002 ). These effects also appl y to adv anced assistance
sys tems in v ehicles (Brookhuis etal. 2001 ; de W inter etal.
2014 ). If these t heore tical constr ucts appl y to the vehicles
with advanced driv er assistance systems, the ques tion that
is at the core of ongoing research is: ho w long do dr iv -
ers require t o regain control of v ehicles saf ely (Gold e t al.
2013 )?
* Ale xander Lotz
rene_ale xander .lotz@daimler .com
Nele R usswinkel
nele.r usswinkel@tu-ber lin.de
1 Daimler T r uck A G, Stuttgart, Ger man y
2 T echnisc he Univ ersität Berlin, Marchs tr . 23, Sekr . MAR 3-2,
10587Berlin, Germany

734 Cognition, T echnology & W ork (2020) 22:733–744
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The tr uc k context does not contain t he amount of pub -
lications as the passenger car conte xt does f or Lev el 3, as
studies of take-o ver are often limited to instances without
high time–pressure (Zhang et al. 2017 ). The effect of lear n -
ing take-o v er situations (Lotz et al. 2019b ), t he influence of
accustoming to a ne w automation function in e xper imental
conte xts and reacting to a ne w w ar ning are influcences that
ha ve been in ves tigated sporadicl y in recent y ears (Kanto witz
etal. 2009 ) and will be the main topic of the present study .
Recent resear ch b y the AD AS&ME project also addressed
the specific issue of dev eloping new driver monitoring sys -
tems to adjust human–mac hine inter f ace content in the tr uc k
on the basis of dr iv er needs (Ax elson et al. 2018 ). This is
impor tant as most par ticipants eng age with the different
automation functions f or t he first time in published researc h
and in most studies onl y a handful of t ake-o ver situations are
presented. Distinguishing under lying human capabilities that
can or cannot adjus t ov er time is paramount.
The aims of the present study are to in ves tigate the
beha vior of semi-tr uck drivers in a pr ototypic v ehicle in
quasi real-w orld Le vel 3 automation on a test trac k. This
will allo w insight into driver b eha vior outside of simu -
lated en vironments and possibl y g enerate other results as
pre viously de ter mined. Different take-o v er situations will
be in v estig ated t hat v ar y in cr iticality to de ter mine possi -
ble differences in dr iv er beha vior at t ake-o ver when a non-
dr iving-related task (NDR T) is per f ormed. The occasion of
these t ak e-ov ers will also be controlled, to in v estig ate the
effect of take-o v er e xpect ancy . The f ollowing introductory
sections f ocus on t he cur rent state of researc h of reaction
times in cr itical take-o v er situation (Sect. 1.1 ), the effect of
e xpected w ar nings (Sect. 1.2 ), the resulting aims of the study
(Sect. 1.3 ), the definition of reaction times (Sect. 1.4 ) and
h ypotheses (Sect. 1.5 ).
1.1 Reac tion timesincritical take‑ ov er situations
Multiple studies ha ve in v estigated reaction times in recent
y ears, pr imar il y in fixed and mo ving-base simulators
(McDonald et al. 2019 ). Results indicate a larg e v ar iation in
reaction times ranging betw een 3 to 8 s according to V ogel -
pohl et al. ( 2016 ) and with an a v erage reaction time until
control is reg ained of M = 2.96 s (SD = 1.96 s) (Eriksson
etal. 2017 ). As pointed out r ightl y by Zeeb e tal. ( 2015 ), it
does not seem as though reaction times are comparable due
to the larg e v ar iance in influencing f actors that ha ve been
categor ized into clusters b y V ogelpohl et al. ( 2016 ): dr iv er ,
en vironmental, v ehicle and human–machine interaction f ac-
tors. All of these studies mentioned w ere conducted within
the passenger car conte xt. A study of time critical t ake-o vers
dur ing Le v el 3 driving in t he tr uc k conte xt pro vided dis -
tinctl y quic ker r eaction times wit h 99.7% (3σ-Interval) of
755 take-o v ers ranging betw een 0 s and 2.82 s (Lotz etal.
2019b ). A possible e xplanation f or these quic k reaction
times could be that within t he tr uc k context driv ers are pro -
f essionals, wit h f ar more e xper ience controlling a v ehicle,
higher mileag e, and dail y repetition. Surpr ising cr itical take-
o v er situations seem to reduce the reaction times also in the
car conte xt, as Dieder ichs e t al. ( 2015 ) deter mine a reduc -
tion of 200 ms compared to e xpected take-o v ers. Previous
w ork also in ves tigated the possibility of predicting reaction
times based on v ehicle sensor data and a dr iver monit or -
ing sys tem (Lotz etal. 2019a ). Limited predict ability w as
achie ved due to res tr icted data and low v ar iance between
prediction classes. A comparison study betw een the pas -
senger car conte xt and tr uck conte xt inv estigating the dif -
f erence in reactions has not been conducted to the best of
the authors ’ know ledge. The de tailed analy sis of cur rent
publications also presents the difficulty of comparability of
results, as e xper imental design and measures vary through -
out all studies.
1.2 Expec ted w arnings andimpac t oftrained
transitions
Researc h on advanced driver assistance sy stems of Le vel2
(S AE J3016 2018 ) has sho wn that reaction times to star t a
motoric response is influenced by the e xpectation of an ev ent
occur r ing (R uscio et al. 2015 ). How e v er , while an une x -
pected situation arose in the abo v ementioned experiment al
real-lif e study , differences in criticality were not in v estigated.
In addition, repetition of unf oreseen ev ents has t hus f ar not
been conducted, eliminating the possibility of obser ving
lear ning of suc h situations (Kantowitz e t al. 2009 ). Melcher
et al. ( 2015 ) in ves tigate an identical take-o v er situation while
v ar ying integration of a NDRT and automated br aking. Pr ior
lear ning of req uests to inter v ene (RtI) has displa y ed posi -
tiv e effects on take-o v er per f or mance. A dditionally , the tr ust
in automation seems to increase after an adequate stag e in
which driv ers can accustom to a ne w function (Her geth e tal.
2016 ). Fur ther research on f alse warnings has also shown
that f alse w ar nings can affect subsequent reactions neg a -
tiv el y (Lees et al. 2007 ). Theref ore, an unansw ered researc h
ques tion that ar ises is ‘how learning of expected situations,
not w ar nings, and the cr iticality of t hese take-o v ers ma y
affect reaction times ’? W e thereby differentiate tr aining from
e xpectation. While we define t he e xpectancy to w ards a take-
o v er situation as t he cur rent state of the dr iv er anticipating a
take-o v er situation will ar ise within a shor t timeframe. This
assumption b y the dr iv er is f or med based on the sur round -
ing of the vehicle, e xplicitly the approac hing to w ards a steep
cur v e in this study as descr ibed in Sect. 2.2 . The opposing
construct of training is the effect of impro ving take-o v er
procedure through learning. While t his study entails aspects
of task switching and the cur rent identification of situation
a wareness, these constructs will not be measured e xplicitly .

735 Cognition, T echnology & W ork (2020) 22:733–744
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1.3 Scope ofstudy
The scope of the present study can be categor ized into tw o
main aspects.
1. A test-trac k study in a pro totype v ehicle pro viding the
possibility of Le vel 3 aut omation wit h critical t ake-o vers
is in v estig ated. By allowing participants to dr iv e on a
high-speed o val tes t-trac k wit h traffic, highl y realistic
highw a y scenar ios are in v estigated with a pro totypic
v ehicle rather t han utilizing a simulator . This allo w s
a compar ison to pre vious simulator results reg arding
dr iv er beha vior f or cr itical take-o v er situations f or tr uc k
dr iv ers. A dditionally , possible shor tcomings of simula -
tor studies suc h as motion sic kness and dr iving comf or t
as mentioned b y Bellem et al. ( 2016 ) can be larg el y dis -
regarded f or the results of this study . Finally , traffic on
the test trac k increases comparability to real-w orld dr iv -
ing on highw ay s, as driver ’ s are inclined to obse r v e their
sur roundings during automation due to real rather t han
simulated v ehicles. This generates im por t ant beha vioral
data regarding real-w orld take-o v er reactions. Due to
saf ety regulations on the test trac k, no obstacles could
be tossed in front of the v ehicles at such high v elocities
with sur rounding v ehicles. Cr iticality , theref ore, was
induced through fictiv e malfunction of the automation
function, i.e., b y prompting a lateral steering swerve
maneuv er .
2. The test scenar io intends to displa y two different take-
o v er w ar nings throughout the dr ive r esulting in t hree
different take-o v er situation types. While all w ar ning
types are introduced in a preparatory tutor ial phase, onl y
the t ak e-ov er type wit h the lo wes t cr iticality , in which
these w ar nings will be initiated, is trained. This will lead
to a discrepancy betw een lear ned beha vior f or different
w ar nings and the cur rent take-o v er situation witnessed.
It is to be e xpected that t his discrepancy causes differ -
ences in reaction times as e xplained in Sect. 2.2.5 and
to adjust due to learning throughout the dr ives.
1.4 Reac tion time definition
The reaction times collected within this study are defined
in accordance with the wor k of Damböck ( 2013 ). Common
take-o v er quality measures suc h as lateral v ar iance in trajec -
tor y after take-o v er will not be repor ted, as precise localiza -
tion w as not possible in the pro totype v ehicle.
Time to e yes on road (TTEoR): the time from the begin -
ning of the auditor y and visual w ar ning until the first fix ation
is star ted within the windscr een area of interest. This serves
as a measure f or cognitive pr ocessing and visual reaction.
Time to hands on s teer ing (TTHoS): the time is defined
from the beginning of the auditor y and visual w ar ning until
hands touc h t he steering wheel f or the first time. The meas -
ure e xpresses the motor ic reaction time needed f or t he driver
to regain the possibility of intervening in the lateral control
of the v ehicle.
Time to firs t reaction (TTFR): tw o thresholds are moni -
tored to define the minimal reaction time measure. The
minimum of the time required f or pressing the ac-/decel -
erator pedal more than 5% or appl ying more than 1 Nm at
the steer ing wheel defines the TTFR. The metric allow s f or
the inter pretation of the time required b y a dr iv er to f or m a
situation a wareness and plan a r oute to circum vent possible
obstacles in the trajector y of a v ehicle.
1.5 Hypotheses
Based on the aims of the study in Sect. 1.3 , the follo wing
h ypotheses are generated. While the data ent ail f ar more
inf ormation, t he study aims are reduced to the f ollowing
f our hypotheses.
1. T ake-o ver situations of higher criticality will promote
quic ker first reactions (TTFR), see Sect. 1.4  f or defini -
tion. This does not include the time to hands on steering
(TTHoS) which will be cons tant f or all t ak e-ov er situa -
tions.
2. A reaction to e xpected situations will occur quic ker than
to une xpected situations, so long the cr iticality is similar .
This is because reactions to w ards e xpected situations
can be planned pr ior to the w ar ning itself. Expected and,
theref ore, anticipated take-o v er situations will yield no
difference in reaction times.
3. Reaction times will be within a similar scope to the
results obtained in the previous simulator study (Lo tz
et al. 2019b ). The magnitude of the a verag e time to first
reaction will be under 2 s f or highly critical t ake-o ver
situations with a NDR T .
4. The engag ement in the NDRT will cause the TTFR to
be slo wer if the NDR T is not addressed.
2 Methods
2.1 P ar ticipants
A total of 27 par ticipants w ere recr uited through direct
inquiry at 60 hauler and logistic fir ms. Participation w as
v oluntar y; all participants were prof essional tr uc k dr iv ers,
receiving a financial incentiv e. Se ven participants were
e x cluded from the dat a anal ysis due to tec hnical sync hro -
nization f ailures of the ey e-track er with t he v ehicle data
and one par ticipant mentioning discomf or t dr iving wit h the

736 Cognition, T echnology & W ork (2020) 22:733–744
1 3
automated function. The e xper iment could be ter minated
at an y point in time and all par ticipants ga v e an inf or med
consent to the e xper iment. It w as explained in de tail t hat
the tr uck w as fitted with a prototypic function and w ar n -
ings, which could arise at an y moment in time. Phy sical
w ellbeing w as addressed and queried pr ior to the star t of
the experiment and monitored dur ing procedure. In addition,
the experimental design under went an internal audit by an
ethical steering committee.
The remaining 20 par ticipants w ere all male with a mean
age of M = 47.1y ears (SD = 12.0 y ears) and reached an a v er -
age annual mileag e of 63,770 km/y ear (SD = 38,732 km/
y ear). A self-estimated 32.3% of the annual mileage w as
spent on highw a ys, 42.0% w ere driven on o verland r oads,
and the remaining 25.7% w as dr iving in urban settings. In
the sample of this study , one par ticipant w as not f amiliar
with speedometers or adaptiv e cr uise control, while o v er
90% used these systems dail y .
2.2 Appara tus
2.2.1 V ehicle
A Mercedes-Benz Actr os 1845 including a prototypic auto -
mation function enabling Le vel 3 on highw a ys w as provided
f or all dr ives. Contr ols of t he automation function w ere iden -
tical to that of the ser ies Activ e Dr ive Assist (Daimler 2018 ),
on the r ight-hand side of the steer ing wheel. The automation
function w as engag ed through the ‘Set ’ button to the cur rent
speed and could be toggled to the desired speed through plus
or minus controls. The second option of eng aging t he auto -
mation function w as initiated through the ‘Resume ’ button,
reactiv ating the previousl y set speed, see Fig. 1 . Automa -
tion could be ter minated at an y moment in time through the
‘OFF’-button.
Once Le vel3 dr iving w as activated, the v ehicle con -
trolled the lateral and longitudinal maneuv er ing, kept the
v ehicle within lane markings, and decelerated if obstacles
appeared ahead. Lateral control req uired the input of lane
markings to a camera and a radar pr ovided objects in the
sur roundings, to brak e accordingl y . Braking w as not nec -
essar y within the experiment al setting, as the tr uc k w as the
slo wes t v ehicle on t he test tr ack. All surrounding v ehicles
w ere steered b y dr iv ers with saf ety training.
2.2.2 T rack
All par ticipants w ere in vited to the automotiv e testing
ground in Papenbur g, Ger man y (A TP Automo tiv e T est -
ing Papenbur g GmbH 2018 ) and ins tr ucted on all saf ety
regulations. The testing ground includes a high-speed o v al
with a length of 12.3km and fiv e lanes f or simulated high -
w a y dr iving. Due to regulations on the trac k, the minimal
dr iving speed w as 90 km/h, which all dr iv ers f ollow ed
dur ing the dr iv es. The Lev el 3 driving w as only initial -
ized on the 4-km-long straights, while the steep curves
w ere dr iv en manually on the lo w est lane. The study w as
conducted in December , which included q uic kl y changing
w eather including sunshine, heavy r ain, strong winds, and
o v ercast skies.
2.2.3 Ey e ‑tracker
Gaze beha vior was trac ked through a Smar t Ey e Pro e y e-
trac ker . Mounted on the dashboard, f our lenses w ere dis -
persed around the dr iv er to monitor gazes into the wind -
screen, onto the tablet, into the instr ument cluster and
to w ards both rear -vie w mir rors. The sys tem w as utilized to
record the visual attention to w ards a non-dr iving-related task
dur ing procedure. The sam pling rate w as 60Hz.
Fig . 1 (Left) Coc kpit ov erview with Smar t Ey e Pro ey e-track er and mounted t ablet. Right: de tails of automation controls on the steer ing wheel
and the tablet providing the non-driving-related task (NDRT)

737 Cognition, T echnology & W ork (2020) 22:733–744
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2.2.4 N on‑ driving‑relat ed tasks (NDRT )
Dur ing activ ation of the automation function, interaction
with NDR T w as a vaila ble. Par ticipants w ere not encour -
aged t o attend t he NDR T , to allo w them to allocate atten -
tion freel y . This resulted in some dr iv ers not attending the
NDR T throughout Lev el 3 automation. The affected trails
w ere handled separatel y dur ing data analy sis.
The NDR T w as based on a pre viously de veloped interac -
tiv e geograph y quiz (Lo tz et al. 2019b ). A contoured map of
Ger man y w as display ed on a mounted t ablet in t he central
console, see Fig. 1 . The task consisted of locating cities on
the map by se tting coordinates through touch. Chosen spe -
cificall y to engag e par ticipants f or longer periods and allow
non-fluent speakers to inter act in a quiz, this task prov ed as
highl y engaging in pre vious studies. T o interact with t he
NDR T , visual and motoric attention was r equired. Through
the new en vironment, the possibility of driving a highl y
automated v ehicle, the necessity to dr iv e manuall y in the
cur v es and the no vel tes t trac k en vironment f or each partici -
pant, w e are confident that passive f atigue did not occur , as
discussed b y Marberg er et al. ( 2018 ). Ho we ver , t his w as not
controlled and will not be addr essed fur ther .
2.2.5 T ake ‑ ov er situations andwarnings
Three different take-o v er situations w ere in v estigated in the
present study with v ar ying time cr iticality . In eac h situation
type, one of tw o optical–acoustic w ar nings w as presented.
One possible combination w as not tested; this resulted in
three take-o ver situations: (1) y ellow w ar ning with low situ-
ation cr iticality , (2) red w ar ning with low situation critical -
ity , and (3) red w ar ning wit h high situation criticality . A
y ello w w ar ning w as onl y presented at the end of a straight
and red w ar nings could appear at an y moment due to sys tem
limits being breac hed.
1. Y ello w take-o v er situation: at the end of each straight, a
y ello w t ake contr ol w ar ning with a 10-s countdown w as
displa y ed in the instr ument cluster . The w ar ning con -
sisted of a periodical tone with 200bpm at 1200 Hz wit h
a y ello w icon in the instr ument cluster , see (4) in Fig. 2 .
As Le vel3 w as not a v ailable in the track’ s steep tur ns,
this automatic prompt w as alwa ys displa y ed. T o let the
dr iv ers accustom to the v ehicle and sur roundings, this
w ar ning w as not displa yed on S traight #1. The reason f or
the yello w warning due to the steep cur v es w as pro vided
to all par ticipants. The y ellow w ar ning w as, theref ore,
repetitiv e and could be expected b y par ticipants. The
cr iticality w as low , as t here w ere more than 10 s until the
steep curve beg an. Participants were instructed to tur n
off the automation function and continue dr iving manu -
all y when the yello w w ar ning w as display ed t hroughout
the complete curv e and reactiv ate automation on the f ol -
lo wing straight.
• Y ellow w ar ning: occur r ing at the end of straights as
an indication to regain manual contr ol with at least
10 s headw a y . The v ehicle continued on its trajector y
with no perceivable incr eased cr iticality .
2. Red tak e-ov er situation: consisted of a red take control
w ar ning presented when the v ehicle reached sy stem
limits and a continuous tone at 1200 Hz occur red, see
(5) in Fig. 2 . Participants were ins tr ucted to take bac k
control of the v ehicle as soon as possible when this sig -
nal appeared, as the system reac hed its limit. The red
w ar ning w as presented multiple times during Lev el 3
activ ation, see Fig. 3 . The automation function regis -
tered the take-o ver at the steering wheel upon which the
w ar ning terminated inst antl y . Par ticipants did not need
to deactiv ate the function wit h the ‘OFF’-button. A ddi -
tionall y , the final yello w warning on Straight #9 w as
substituted with this red take-o v er w ar ning, to in v esti -
gate the response to w ards switc hed w ar ning types and,
Fig . 2 A utomation system w ar nings in the instr ument cluster betw een
speedometer and re v counter . Lef t to right: (1) automation a vailable
and ready f or activ ation. (2) Automation function activ ated as sym-
bolized through blue ‘HP’ symbol. (3) Activ ation function passiv e
due to o vers teer ing b y par ticipants. (4) Y ello w w ar ning at the end of
all straights with a 10-s counter . (5) Red w arning advising immediate
take control (color figure online)

738 Cognition, T echnology & W ork (2020) 22:733–744
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theref ore, an une xpected w ar ning signal with identical
cr iticality
• R ed w ar ning: occur r ing an ywhere on the straights
with the instr uction to regain control as q uic kly as
possible. The v ehicle continued on its trajectory with
no perceiv able increased cr iticality .
3. Lane twitc h take-o v er situation: instead of w ar ning wit h -
out seeming en vironmental reason and lo w cr iticality , a
rapid trajectory chang e w as induced to gener ate a t ake-
o v er situation with high cr iticality and t he red w ar n -
ing w as displa y ed. This w ar ning w as onl y induced on
the straights without the need to regain manual control
immediatel y in front of steep curve, due to saf ety rea -
sons. This take-o v er type is ref er red to as a lane twitc h
hereon.
• R ed w ar ning and lane twitc h: occur r ing dur ing auto -
mation an ywhere on the straights with the instr uction
to regain contr ol as quic kl y as possible. The v ehicle
trajectory chang ed rapidl y to increase cr iticality .
The trajectory chang e induced resulted in a steer ing
wheel angle c hange (M = 18.68°, SD = 2.81°) to the r ight,
a trac k offset (M = 0.21 m, SD = 0.12 m) and lateral accel -
eration (M = 0.60 m/s 2 , SD = 0.25 m/s 2 ). A saf ety engineer
seated on the rear seat behind the par ticipants obser ved tr af-
fic in the rear -view mirrors and did not star t the take-o ver if
v ehicles wer e wit hin the pro ximity .
2.3 Proc edure
After completing all saf ety introductions par ticipants filled
out a socio-demographic questionnaire, the automation func -
tion and NDR T wer e descr ibed and one lap on the test trac k
as a passenger w as completed. During all dr ives, one saf ety
engineer w as present in the v ehicle and an additional experi -
menter guided the par ticipants through t he tutorial and dr ive.
Dur ing the introductory lap, t he automation function w as not
sho wn; how ev er , the controls were clarified at the steer ing
wheel and function-related ques tions wer e answered. The
par ticipants had no specific instructions f or t he drive o ther
than to experience a proto typic automation function no v -
ices but with a prof essional bac kground and high driving
e xper ience.
Participants were in control of the v ehicle f or fiv e laps
(Straight#0–#9), of which the first tw o laps consisted of a
guided tutor ial to accust om to t he automation function. The
automation function w as initiated on Straight #1 f or the first
time, see Fig. 3 . Dr iv ers could o verr ule t he function at an y
time b y pressing the ac-/decelerator pedals or guiding t he
v ehicle laterall y with t he steering wheel wit hout turning t he
automation function off, see (3) in Fig. 2 . While t he automa -
tion function w as being o v er r uled, the function was passiv e
and reinitiated itself upon terminating t he o v er r uling. Dur -
ing the first f our straights, the par ticipants e xper ienced a
total of se v en t ak e-ov ers (t hree yello w , f our re d ), see Fig. 3 .
The first r ed t ak e-ov er warning on each s traight dur ing the
tutor ial phase w as alwa ys announced prior to t he take-o v er .
On Straight#3, t he NDR T w as activ ated the first time and
the red w ar ning symbol w as displa y ed on the t ablet during
the request t o inter v ene. The yellow take-o ver situation w as
announced in the tutor ial phase to accustom participants to
tur n off the Lev el3 aut omation when approaching the steep
cur v es.
The actual e xper iment phase began with the approach
onto Straight #4. The participants dro ve one of tw o sce -
nar ios in the e xper iment phase in which the re d and lane
twitc h take-o v ers on Straight#4, #7 and #8 w ere re v ersed,
see Fig. 3 . The re versal of the tw o scenar ios w as aimed at
in v estig ating the influence of t he tutor ial phase on kno wn
take-o v er scenar ios, re d take-o ver , and unknown critical
take-o v ers, lane twitc h , directl y af ter the tutor ial phase. The
order of the time cr itical take-o v ers is depicted in Fig. 3 fo r
both scenar ios (V1 and V2). The participants activated the
automation function at the beginning of each s traight and the
NDR T w as unloc ked. The fiv e laps w ere completed within
appro ximatel y 45 min. Due to the f act that sunset w as at
16:15 (4:15pm); it w as possible to collect data in t he earl y
phases of night as tw o dr iv es star ted after 16:30 (4:30 pm)
each day .
Fig . 3 Ov erview of the procedure of par ticipants with the chronology
of take-o ver types during Lev el 3. Three different take-ov er situations
wer e presented: yello w , red and twitch (lane twitc h) scenarios. The
procedure w as completel y identical e xcept f or the t ake-o ver situations
on Straight#4,#7 and #8 in whic h each driver dr ov e eit her v ar iant V1
or V2 (color figure online)

739 Cognition, T echnology & W ork (2020) 22:733–744
1 3
3 Results
The study design is a within-subject design with repeated
measures resulting in 356 take-o v ers. The v ehicle dat a,
e ye-tr acking and video recordings w ere synchronised
and anal yzed with respect to t he f or mulated hypo t heses
of Sect. 1.5 . Due to the f act t hat the proto typic vehicle
w as not fitted with a capacitiv e steering wheel, t he time
to hands on steering (TTHoS) were e v aluated from a syn -
chr onised video of the dr ives. F or the time to first reaction
(TTFR), the threshold of 1 Nm at the steering wheel or
5% ac- or decelerator pedal position c hange w as defined.
Figure 4 depicts TTFR f or both phases of the dr ive
(tutor ial/e xper imental) f or each tak e-ov er situation type,
when dr iv ers had their hands off the steer ing wheel. For
the experimental phase, all reaction times are documented
in T able 1 . On a v erage during the experiment phase, t his
resulted in a TTFR (M = 1.087 s, SD = 0.417 s), TTHoS
(M = 0.727 s, SD = 0.253 s), and TTEoR (M = 0.364 s,
SD = 0.425 s) f or a total of 162 t ake-o vers in whic h the
hands w ere off the steer ing. An anal ysis of all take-o v ers
through a full f actor ial ANO V A wit h f actor phase (tuto -
r ial, e xper iment), situation type (y ello w , red, lane twitch),
hands on steering (tr ue, false), and da ytime (da y , night)
w as conducted f or TTFR and TTHoS. The results of
both ANO V A are presented in T able 2 . An ov er view of
the quic kes t and slow est reaction times is also giv en in
T able 1 . F or some considered take-o ver situations of situ -
ation type yello w and re d , TTEoR and TTFR of 0s were
recorded. This displa ys tak e-ov er situations in which the
dr iv er had their ey es on t he road and w as not engaging
in the secondar y task. For t he yello w situation types this
TTEoR w as 0 s in 33 take-o v ers, while in the re d situa -
tion types this only occurred once, displaying anticipation
of a take-o v er situation. Even the maximal outliers dis -
pla y v er y quic k reaction times f or all t ak e-ov er situations.
No tably , the maximum TTEoR are slo w er t han TTFR and
TTHoS. This can be attr ibuted to drivers f ocusing on the
instrument cluster with t he hands already being placed on
the steer ing. The anal ysis of bo t h the TTFR and TTHoS
por tra ys that there is a significant effect of drive phase in
Fig . 4 Left: time to first reaction (TTFR) in the tutorial and experi-
mental phase when hands were not guiding the steering wheel
f or each different w ar ning type (i.e., yello w , red and red, and lane-
twitch). Right: time to firs t reaction (TTFR) f or the two different v er-
sions with respect to the first take-o ver compared t o all other t ake-
o vers of the same situation type (color figure online)
T able 1 Time t o first reaction, time to hands on steering and ey es on road in e xper iment phase
Minimum, maximum, and a v erage times are reported
Situation type # of take-o vers Time to first reaction (s) Time to hands on steering (s) Time to e y es on road (s)
Min Max Avg Min Max Av g Min Max Av g
Y ello w 67 0 2.31 M = 1.222 (SD = 0.403) 0.06 1.65 M = 0.705 (SD = 0.300) 0 2.28 M = 0.324 (SD = 0.449)
Red 53 0 2.31 M = 1.109 (SD = 0.469) 0.36 1.56 M = 0.772 (SD = 0.244) 0 2.01 M = 0.496 (SD = 0.469)
Lane T witch 42 0.36 1.58 M = 0.841 (SD = 0.233) 0.36 1.56 M = 0.705 (SD = 0.173) 0 0.78 M = 0.262 (SD = 0.267)

740 Cognition, T echnology & W ork (2020) 22:733–744
1 3
the dat a, this is to be e xpected as dr iv ers were ins tr ucted
and no vices in the tutor ial phase. If the hands of t he dr iv er
w ere on the steer ing, this resulted in a highly significant
main effect f or both reaction times. A small effect is regis -
tered f or t he f actor criticality (yello w/red vs. lane twitc h),
while the expectancy (y ello w vs. red) yielded no effect f or
TTFR. Three fur ther mild interaction effects wer e identi -
fied f or TTFR. These results lead to a rejection of Hypoth -
esis #1, as no clear indication of e xpectancy and cr itical -
ity is identified f or TTFR. Contrar ily , a highly significant
main effect f or expectancy (yello w vs. red w ar ning) was
identified f or TTHoS, while t he data did not displa y an y
significant differences f or cr iticality , see T able 2 .
As par ticipants dro v e one of tw o v ersions, see Sect. 2.3 ,
a v ersion comparison w as conducted f or the first take-
o v ers in compar ison to all other take-o v ers in the experi -
mental phase. The compar ison aims to identify whether
the nov el situation lane-twich (M = 0.807, SD = 0.118)
caused prolong ed TTFR compared to all other lane-
twitches (M = 0.725, SD = 0.340), see F ig. 4 . As the red
w ar ning w as lear ned in t he tutorial phase par ticipants were
f amiliar with t his situation type. Due to uneq ual sample
T able 2 R esults of full-fact or ial ANO V A f or TTFR and TTHoS
Dependent v ar iable Measure Sum of squares d f Mean square F Pr (> F) Partial
𝜂 2

Time to first r eaction Phase 12.59 1 12.59 43.43 1.69e-10 0.114167
Expectancy 0.93 1 0.93 3.203 0.07441 0.009414
Cr iticality 2.60 1 2.60 8.974 0.00294 0.025939
Hands on steering 34.91 1 34.91 120.5 < 2e16 0.263357
Da ytime 0.00 1 0.00 0.016 0.90028 0.000047
Phase * e xpect ancy 0.08 1 0.08 0.280 0.59711 0.000830
Phase * hands on steering 3.15 1 3.15 10.87 0.00108 0.031260
Expectancy * hands on steer ing 2.16 1 2.16 7.441 0.00671 0.021603
Criticality * hands on steer ing 0.03 1 0.03 0.106 0.74553 0.000313
Phase * da ytime 0.29 1 0.29 0.998 0.31843 0.002954
Expectancy * Daytime 0.22 1 0.22 0.752 0.386601 0.002252
Criticality * Daytime 0.00 1 0.00 0.000 0.98561 9.67e-07
Hands on steering * daytime 3.84 1 3.84 13.25 0.00032 0.037824
Phase * e xpect ancy * hands on steering 0.04 1 0.04 0.135 0.71391 0.000399
Phase * e xpect ancy * da ytime 0.00 1 0.00 0.006 0.93690 0.000019
Phase * hands on steering * daytime 0.36 1 0.36 1.243 0.26563 0.003676
Expectancy * hands on steer ing * da ytime 0.21 1 0.21 0.716 0.39822 0.002119
Phase * e xpect ancy * hands on S.* da ytime 0.00 1 0.00 0.000 0.99852 1.02e-08
Time to hands on steering Phase 1.288 1 1.288 19.985 1.07e-05 0.055982
Expectancy 4.509 1 4.509 69.968 1.62e-15 0.171925
Criticality 0.013 1 0.013 0.204 0.6517 0.000605
Hands on steering 27.29 1 27.29 423.51 < 2e-16 0.556880
Da ytime 0.339 1 0.339 5.260 0.0224 0.015368
Phase * e xpect ancy 0.015 1 0.015 0.226 0.6347 0.000671
Phase * hands on steering 0.156 1 0.156 2.414 0.1212 0.007113
Expectancy * hands on steer ing 0.028 1 0.019 0.295 0.5132 0.001270
Criticality * hands on steer ing 0.010 1 0.010 0.162 0.6877 0.000480
Phase * da ytime 0.003 1 0.003 0.040 0.8418 0.000118
Expectancy * daytime 0.009 1 0.009 0.143 0.7052 0.000425
Criticality * daytime 0.000 1 0.000 0.005 0.9463 0.000014
Hands on steering * daytime 0.062 1 0.062 0.956 0.3289 0.002828
Phase * e xpect ancy * hands on steering 0.000 1 0.000 0.000 0.9988 7.04e-09
Phase * e xpect ancy * da ytime 0.028 1 0.028 0.439 0.5082 0.001300
Phase * hands on steering * daytime 0.001 1 0.001 0.012 0.9144 0.000034
Expectancy * hands on steer ing * da ytime 0.001 1 0.001 0.011 0.9153 0.000034
Phase * e xpect ancy * hands on S.* Da ytime 0.004 1 0.001 0.056 0.8137 0.000165

741 Cognition, T echnology & W ork (2020) 22:733–744
1 3
size homoscedasticity w as tested through a lev ene test with
f actors situation type and the binar y f actor first take-o v er
or all others f or t he dependent v ar iable TTFR. As homo -
scedasticity w as not achie v ed F(3,65) = 2.63, p = 0.058, a
W elch test w as per f or med displa ying significance onl y f or
situation type F(1,65) = 17.617, p < < 0.001. The results
displa y no difference with respect to first appearance of
take-o v er situations, showing that the highl y cr itical lane
twitch situation w as attended unconditional of no velty .
With respect to the re d situation type, non-significant
results strengthen the assumption that lear ning of take-
o v er procedure in the tutor ial phase w as effectiv e.
Similarl y , on the last s traight of the experimental phase
(Straight #9), dr iv ers e xper ienced a different e xpected
w ar ning, a red take-o ver w ar ning w as displa yed ins tead
of a y ello w w ar ning. Figure 5 (left) depicts t he TTFR
f or all yello w w ar nings on Str aight #8 compared to the
no v el red w ar ning at the end of Straight #9. A tw o-w a y
ANO V A f or TTFR was calculated with the categor ical
v ar iable whether hands w ere touching the steering or not
and the situation type. This resulted in a significant effect
f or hands on steer ing F(1,36) = 18.67, p = 0.0002, par -
tial-
𝜂 2

= 0.329, but not f or situation type F(1,36) = 0.349,
p = 0.558, par tial-
𝜂 2

= 0.0091.
Also in Fig. 5 (r ight), TTFR is presented f or t he
three different situation types and whether par ticipants
engag ed in the NDRT within 2 s pr ior to RtI. Eng age -
ment w as determined t hrough e ye-trac king dat a and if
the t ablet w as touched within this timeframe. A one-w a y
ANO V A f or NDRT eng agement w as calculated f or TTFR
with no significant effect, F(1,162) = 0.004, p = 0.948,
par tial-
𝜂 2

= 0.00002.
4 Discussion
Multiple researc h questions are addressed in the present
study concerning t ake-o ver beha vior of prof essional tr uck
dr iv ers in Le v el3 automation. T est trac k dr iv es, simulating
highw a y scenar ios, with non-affiliated prof essional tr uck
dr iv ers w ere in ves tigated to identify r eactions to w ards dif -
f erent warning types and cr iticalities resulting in take-o v er
situations. The inf or mation gathered is paramount to design
future dr iv er assistance sys tems, enhance usefulness (DIN
EN ISO 9241-210:2010 2010 ) and appl y a human-centered
design approac h (Coole y 1982 ). A positive b yproduct of
appl ying this know ledge to the design process of future sy s -
tems is to ac hiev e t he goal of reducing w orkload on drivers
and therein decrease accidents.
4.1 T ake ‑ ov er criticality
One of the pr ime f ocuses of this study w as to identify
dr iv er beha vior f or varying cr itical take-o ver situations in
a real v ehicle. The statistical anal ysis of TTFR displa yed a
mildl y significant effect of the two le v els of take-o v er cr iti -
cality , while TTHoS w as const ant f or t his f actor . Cr itical -
ity w as manipulated b y inducing a steering impulse in the
high condition, causing the steer ing wheel to sw er v e to the
r ight when drivers w ere occupied with a NDR T and hands
w ere not on the steer ing wheel. Ho w ev er, no visual obs t a -
cles caused the variation in cr iticality to b ypass any risk
of causing an accident f or t he par ticipants. Dr iv er interac -
tion depending on cr iticality seems to cause a q uick er first
reaction at the controls of the v ehicle, see Fig. 4 . How ev er ,
Fig . 5 Left: time to first reaction (TTFR) f or all expected y ellow w ar ning and expected r ed warning on Straight #9. Right: comparison of TTFR
f or all w ar ning types and whether NDR T w as play ed or not within 2s pr ior to a take-o ver req uest (color figure online)

742 Cognition, T echnology & W ork (2020) 22:733–744
1 3
this does not signify that route planning f or the handling of
the situation w as quic ker . The lane twitch situation in this
study caused an acceleration to be perceiv ed by the dr iv er .
Instinctiv e reaction to the kinaesthesia could ha v e caused
the quic ker time to firs t reaction. Hypothesis 1 is theref ore
f ailed to be rejected, with necessar y future in vestig ations
f ocusingon the causation of quic ker reactions.
4.2 W arning expec tancy
T w o different w ar ning types w ere presented in the three dif -
f erent t ake-o ver situations. The tw o t ak e-ov er warnings con -
sisted of a yello w take control w ar ning issued solely at the
end of a straight and a re d t ak e control w ar ning issued when
sys tem limits were me t and dr iv ers had to reg ain control
immediatel y . These two tak e-ov er situations did not differ
in cr iticality and w ere issued on the straights. In these situ -
ations, the system did no t appear to mishandle the dr iving
task; how e v er, tak e-ov er was req uired (y ello w and red). A
y ello w t ak e-ov er w ar ning w as alw a ys presented at the end of
a straight, allo wing each par ticipant to f oresee (expect) this
combination of situation type and w ar ning when approac h -
ing the steep cur v e. Solel y on the last straight, the y ello w
w ar ning w as replaced wit h a red w ar ning.
The comparison of t he red and y ello w w ar nings displa y ed
no significant difference f or TTFR. Interestingl y , a highl y
significant effect f or expectancy w as identified f or TTHoS.
As the yello w warning could be expected at the end of the
straights, take-o ver procedure could be anticipated at the end
of the straights. This is also p resent in the dat a, as at r oughly
50% of all yello w t ak e-ov er situations, the ey es w ere already
f ocused on t he road, demonstrating e xpect ancy . The o verall
tendency of e xpectancy to ha ve no effect on TTFR w as also
identified f or t he first and last tak e-ov ers in t he e xper iment
phase in which ne w and switched w ar nings w ere presented,
respectiv el y . The discrepancy betw een TTFR and TTHoS
regarding e xpect ancy can ha ve multiple reasons. Ho we v er ,
pr imarily onl y placing the hands at the steer ing caused the
w ar ning to terminate. The effect of expectancy on TTHoS
is, theref ore, anticipated. As a clear dr iv er interaction based
on ob verting an obstacle at t ak e-ov er w as not necessar y , the
threshold f or TTFR could ha v e been set too high f or t he
reaction to register similar t o TTHoS.
An anal ysis of the final take-o v er w ar ning at the end of
Straight#9, which w as switc hed from an e xpected yello w
w ar ning to a red w ar ning, produced no significant effect f or
TTFR. Ho we ver , reaction times did differ significantl y in
the yello w warning situation when hands were guiding the
steering. A probable explanation is that some participants
e xpected y ellow w ar nings, while others tr usted the automa -
tion function to w ar n them adequatel y possibl y also v er ify -
ing the w ar ning in the instr ument cluster . With the appear -
ance of a different auditor y–visual, w ar ning was e xpected;
ho we ver , t he cr iticality w as not and led to a ree valuation of
the situation. The motoric response to unf oreseeable take-
o v ers did not differ throughout the dr iv es and a v eraged at
0.727 s. A similar conclusion w as dra wn when e xplicitly
in v estig ating the hands-off times in par tially automated traf-
fic jam scenar ios with cr itical take-o v er situations (Naujoks
etal. 2015 ). Theref ore, based on the inf or mation gathered,
Hypothesis#2 is f ailed to be rejected.
4.3 Compar ability tosimulator studies
Another researc h f ocus is to compare pre viousl y gathered
inf or mation in a moving-base simulator to driver beha vior
in a real v ehicle. Generall y , cur rent in v estigation s in t he
Le vel3 context are e xamined e xclusiv ely in simulators, t o
e x clude t he possibility of tec hnical malfunctions or human
er ror causing accidents. This is mainl y because proto types
often are not as sophisticated as series technology and no v el
or e xtreme situations are in v estig ated. While the possibili -
ties in simulated en vironments ha v e ex celled in recent years,
une xpected influences might take an effect on results when
tr ying to e xtrapolate results to a real-w orld context. Suc h
influences could include the intr insic trust of par ticipants
that no direct ph ysical harm will occur in a simulator and the
aspect of treating driving simulators as games (Bellem et al.
2016 ). Ov erall, results obtained in simulators ha v e to be v ali -
dated in controlled and saf e real-w orld scenar ios to manif est
conclusions dra wn from the dat a. As pre vious experimental
results in a tr uc k simulator produced e xtremel y quic k TTFR
(M = 1.35, SD = 0.49) f or 755 highly time-critical t ake-o vers
(Lotz etal. 2019b ), it w as our motiv ation to validate these
findings.
Results in Sect. 3 sho w t hat especiall y the motor ic
response time, TTHoS, w as constant regardless the e xpected
cr iticality . Lear ning w as obser ved fr om the tutor ial to the
e xper imental phase, see Fig. 4 . The TTFR reduced signifi -
cantl y betw een the tutor ial and experimental phase gener -
ating consistent results of learning b eha vior as obser ved
pre viously (Lo tz et al. 2019b ). These near -constant motoric
response times obser v ed in the data cohere wit h the findings
from Zeeb et al. ( 2015 ). Second, the different warning types
yielded a significant effect f or TTHoS in the experimental
phase. This contradicts the results f or TTFR, as onl y the
most critical t ake-o ver situation g enerated a mildl y signifi -
cant decreased reaction time. Ov erall, a TTFR of 1.08 s w as
obser v ed f or all t ak e-ov er situations in which the hands w ere
not guiding the steering wheel. A separate in v estigation of
TTFR f or NDR T engag ement yielded no significant results.
For the consideration of reaction times in the present
study the results of the descr iptiv e and inf erential analy sis
f ail to reject Hypothesis 3 with a 3σ-inter val of 0–2.343 s f or
164 take-o v ers, depicting 99.7% of the dat a. One dra wback
and possible e xplanation of the difference in TTFR could

743 Cognition, T echnology & W ork (2020) 22:733–744
1 3
be the low tor que req uired to take-o v er the v ehicle in t he
y ello w and red w ar ning conditions. Onl y in t he lane-twitc h
condition w as a strong s teer ing wheel torq ue requir ed. It
should be noted that the present study did not present highl y
time cr itical situations due to en vironmental obst acles, but
the steer ing w as manipulated in t he lane-twitc h condition
to induce cr iticality as a saf e option to in v estigate tak e-ov er
cr iticality .
4.4 Non‑driving‑related tasks (NDRT )
Multiple publications in v estig ation t he effect of NDR T dur -
ing Le vel 3 ha v e been published recentl y (Merat et al. 2012 ;
Pe ter mann-Stoc k et al. 2013 ). In the present study par tici -
pants could engag e in a NDR T dur ing Le vel3 automation.
As a measure of engag ement t he visual attention to w ards the
NDR T and tablet interaction w ere consulted, see Fig. 5 . The
data repor ted show s no significant effect of NDRT on TTFR.
This is une xpected and leads to a rejection of Hypothesis 4.
While the results in published research sho w no significant
effects amongst different NDR T (Radlmayr e t al. 2014 ), a
general neg ativ e effect of task s is documented (V ogelpohl
etal. 2016 ). In the present study , participants could choose
freel y if they w anted to attend t he NDR T or not. It is possi -
ble that the self-motivated eng agement w as, t heref ore, lo wer
than in studies in which dir ect t ask instruction w as pro vided.
5 C onclusion
With little e xper ience to lean on, as no studies addr essing
a standardized procedure of in ves tigating take-o v er beha v -
ior in cr itical take-o v er situations e xists, this study presents
no v el insights into the experiment al procedure and results
of near real-w orld take-o ver beha vior f or Le vel 3. A total of
292 take-o v ers in which hand s were no t guiding the steer ing
wheel and 64 with hands on steer ing w ere e xamined. The
inf or mation gathered allow s f or a better understanding of
reaction times and beha vior dur ing take-o v ers to e xpected
and une xpected ev ents. Ultimately , t his is v aluable know l -
edge t o reduce accidents in cr itical take-o v er situations
in which the mac hine and humans share responsibility of
guiding v ehicles saf el y . For optimal data comparability ,
w e would w elcome a definition of t ake-o ver situations and
measures throughout the community .
Compared to pre vious results, the reaction times are in
the low er spectr um of repor ted reaction times (Erik sson
et al. 2017 ). As ar gued pre viousl y , prof essional tr uck drivers
f or m a unique sample as e xper ience and training are high.
It should be clear , ho we ver , t hat no driver had pre viously
dr iv en on this test trac k, the automation function and vehicle
w as uncommon and dr iv ers w ere inclined to act saf el y . The
time to first reaction (M = 1.087 s, SD = 0.417 s), time to
hands on steering (M = 0.727 s, SD = 0.253 s) and time to
e y es on road (M = 0.364s, SD = 0.425s) are most lik ely as
quic k as possible f or the presented scenar ios.
As no obstacles wer e manipulated in the envir onment
of the vehicles at tak e-ov er, the reaction times onl y displa y
the cognitive and mo tor ic response times req uired to react
to w ards a take-o v er signal. R esulting trajectories or displace -
ments within the lane were not in ves tigated. Primar ily due to
shor t distances on the test trac k straights and as participants
reactiv ated the automation function wit hin 10 s, an anal ysis
of take-o v er quality is absent. In conclusion, the collected
reaction times present the quic ker spectr um of take-o v er
capability b y prof essional tr uck drivers. While the limit of
take-o v er reaction times cannot be muc h quic k er, slow er
reaction times due to long automation phases, higher dis -
traction and repetition are v er y likel y and need thorough
in v estig ation in the future. The reaction times here present
the minimum time an automation function needs to span
to assist driver tak e-ov er beha vior . This still disregards if
the dr iv er f eels comf or t able with these t ake-o ver times and
is capable of deliv er ing this high per f or mance ov er longer
per iods of time.
Acknowledgmen ts Open Access funding pro vided by Projekt DEAL.
C ompliance with ethical standards
Conflict of interest Alex ander Lotz and Enrico W ohlf ar th are em-
plo yed b y Daimler A G.
Open Acc ess This ar ticle is licensed under a Creative Commons A ttr i -
bution 4.0 Inter national License, whic h per mits use, sharing, adapt a -
tion, distribution and reproduction in an y medium or f or mat, as long
as you giv e appropr iate credit to the or iginal author(s) and the source,
pro vide a link to the Creative Commons licence, and indicate if c hanges
wer e made. The images or other third par ty material in t his article are
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otherwise in a credit line to t he material. If mater ial is not included in
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copy of this licence, visit http://creat iv eco mmons .org/licen ses/b y/4.0/ .
Refer ences
A TP Automo tive T esting P apenburg GmbH (2018) A utomotiv e T esting
Papenbur g. Citation: 20.02.2019 https ://atp-papen burg.de
Audi (2017) A udi T ec hnology Port al. Citation: 26.02.2019 from Audi
A8 - Audi AI traffic jam pilo t: https ://www .audi-tec hn ology -por ta
l.de/en/elect rics-elect ronic s/dr ive r -assis tant-syste ms/audi-a8-
audi-ai-traffi c-jam-pilot
Ax elson M, Ahlström C, Kr upenia S, Anund A, Leeuw en W , Kec klund
G (2018) Is it possible to adjust the driving and resting times when
operating highl y autonomous tr uc ks? J Sleep Res 27(1):P345
Bainbridge L (1983) Ironies of automation. A utomatica 6:775–779

744 Cognition, T echnology & W ork (2020) 22:733–744
1 3
Bellem H, Kluv er M, Schrauf M, Sc honer H-P , Hecht H, Krems JF
(2016) Can we s tudy autonomous dr iving comf or t in moving-
base driving simulators? A validation study . Hum Factors
59(3):442–456
Brookhuis K, de W aard D, Janssen W (2001) Beha vioural impacts of
adv anced dr iv er assistance systems - an o v er view . Eur J T ransp
Infrastruct Res 1(3):245–253
Coole y M (1982) Achitect or Bee?: Human Price of T echnology (Cur -
rent affairs). Chatto & Windus, UK
Daimler (2018) Mercedes-Benz. Citation: 26.08.2019 from The new
Actros 2 019: https ://www .merce des-benz.c om/en/merce des-benz/
v ehic les/tr uck s/the-ne w-actr o s-2019/
Damböc k D (2013) Automationseffekte im Fahrzeug - v on der Reak -
tion zur Ü bernahme. T echnisc he Univ ersität München, Lehrstuhl
für Ergonomie, Münc hen
de Winter JC, Happee R, Martens MH, Stanton NA (2014) Effects of
adaptiv e cr uise control and highl y automated dr iving on w ork -
load and situation a w areness: A revie w of t he empirical evidence.
T ransp Res Part F 27:196–217
Diederichs F , Bischoff S, R eilhac P (2015) W elchen Einfluss hat das
HMI?. V ergleich v on Ü bergabezeiten in N otf allsituationen bei
S AE Lev el 2 und 3 Automatisier ung mit neuartiger Smar tphone-
Integration im direkten Fahrersic htf eld und mit Lenkradbedie -
nung. Berliner W erkstatt Mensc h-Maschine-Sy steme: Tr ends in
Neuroer gonomics 11:72–79
DIN EN ISO 9241-210:2010 (2010) Ergonomics of human-sy stem
interaction Part 210: Human-centred design f or interactive
sys tems
Endsle y MR (1995) T ow ard a Theor y of situation a w areness in
dynamic sys tems. Hum Factors J 37(1):32–64
Erik sson A, Stanton N A (2017) T ake-o v er time in highly automated
v ehicles: non-critical transitions to and from manual control. Hu m
Factors. https ://doi.org/10.1177/00187 20816 68583 2
Gold C, Damböc k D, Lorenz L, Bengler K (2013) T ake o v er! How long
does it take to ge t the dr iv er back into the loop? Proc Hum F actors
Ergono Soc Annu Mee t I 57:1938–1942
Herg eth S, Lorenz L, Krems JF (2016) Prior Familiar ization with
takeo ver req uests aff ects drivers ’ t akeo v er per f ormance and
automation tr ust. Hum F actors J Hum Factors Er gonom Soc
59(3):457–470
Kanto witz BH, Roedig er HL, Elmes DG (2009) Researc h techniq ues:
e xper iments. W adsw or th Cengag e Lear ning, Belmont
Lees MN, Lee JD (2007) The influence of distr action and dr iving con -
te xt on dr iv er response to imper f ect collision warning systems.
Ergonomics 8:1264–1286
Lotz A, W eissenberger S (2019) Predicting take-o ver times of truck
drivers in conditional autonomous driving. In: Stanton N (ed)
Adv ances in human aspects of transport ation. AHFE 2018.
Adv ances in intelligent sy stems and computing, v ol 786. Springer ,
Cham, pp 329–338
Lotz A, R usswinkel N, W ohlf ar th E (2019) Response times and gaze
beha vior of tr uc k dr iv ers in time critical conditional automated
driving t ake-o v ers. Tr ansp Res Part F 64:532–551. https ://doi.
org/10.1016/j.trf.2019.06.008
Marberg er C, Mielenz H, Naujoks F , Radlmayr J, Beng ler K, W andtner
B (2018) U nderstanding and appl ying the concepf of “dr iv er a v ail -
ability” in automated driving. In: Stantion NA (ed) Adv ances in
human aspects of transpor tation. AHFE 2017. Adv ances in intel -
ligent sy stems and computing, v ol 597. Springer , Cham
McDonald AD, Alambeigi H, Engström J, Mar kk ula G, V ogelpohl T ,
Dunne J, Y uma N (2019) T o w ards computational simulations of
beha vior during automated dr iving take-o vers: a re vie w of the
empirical and modeling literatures. Hum Factors 61:642–688
Melcher V , Rauh S, Dieder ichs F , Widlroither H, Bauer W (2015)
T ake-ov er requests f or automated driving. 6t h international
conf erence on applied human f actors and ergonomics. Pr ocedia
Manuf 3:2867–2873
Merat N, Jamson H, Lai F , Carsten O (2012) Highl y automated dr iving,
secondar y task per f or mance, and dr iv er state. Hum Factors J Hum
Factors Er gonom Soc 54(4):762–771
Naujoks F , Pur uc ker C, Neukum A, W olter S, Steiger R (2015) Con -
trollability of partially automated driving functions–does it matter
whether drivers are allo wed t o t ak e t heir hands off the steering
wheel? T ransp Res Part F T raffic Psyc hol Beha v 35:185–198
Pe ter mann-Stoc k I, Hac kenber g L, Muhr T, Mer gl C (2013) Wie lang e
braucht der F ahrer–eine Analy se zu Ü bernahmezeiten aus ver -
schiedenen N ebentätigkeiten w ährend einer hochautomatisierten
Stauf ahr t. 6. T agung Fahrerassis tenzsysteme. Der W eg zum
automatischen F ahren
Radlma yr J, Gold C, Lorenz L, Farid M, Bengler K (2014) How traffic
situations and non-driving related t asks affect the t ak e-ov er qual -
ity in highl y automated dr iving. Proc Hum F actors Ergonom Soc
Ann Meet 58:2063–2067
Ruscio D, Ciceri MR, Biassoni F (2015) How does a collision w ar n -
ing sys tem shape dr iv er’ s brake response time? The influence of
e xpect ancy and automation complacency on real-lif e emerg ency
braking. Accid Anal Pre v 77:72–81
S AE J3016 (2018) from https ://www .sae.org/misc/pdfs/autom ated_
drivi ng.pdf
T esla (2019) Cit ation: 26.02.2019 from T esla Autopilot https ://www .
tesla .com/de_DE/autop ilot
V ogelpohl T , V ollrat h M, Kühn M, Hummel T , Gehler t T (2016) Ü ber -
gabe v on hochautomatisier tem Fahren zu manueller S teuer ung.
Gesamtv erband der Deutschen V ersicher ungswirtschaft e.V ,
Berlin
W ylie G, Allport A (2000) T ask switc hing and the measurement of
"switch cos ts". Psyc hol Res 3(4):212–233
Y oung M, S t anton N (2002) Malleable attention resources theory: a
new e xplanation for the effects of mental underload on perfor -
mance. Hum Factors 3:365–375
Y oung M, S t anton N (2007) Bac k to the future: brake reaction times f or
manual and automated v ehicles. Ergonomics 1:46–58
Zeeb K, Buchner A, Sc hrauf M (2015) What determines the t ake-o v er
time? An integrated model approach of driver tak e-ov er after auto -
mated driving. Accid Anal Prev 78:212–221
Zhang B, Wilsc hut E, Willemsen D, Martens M (2017) Dr iv er response
times when resuming manual control from highl y automated driv -
ing in tr uc k platooning scenar ios. Conf erence: RSS2017 Road
Saf ety & Simulation international conf erence (The Hague, Neth -
erlands). https ://doi.or g/10.13140 /R G.2.2.28249 .01127
Publisher’ s Note Spr inger N ature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.

Why institutions use Plag.ai for originality review, entry 45

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