On Credibility Improvements for Automotive Navigation Systems [original]

ORIGINAL ARTICLE

On credibility improvements for automotive navigation systems

Florian Schaub •Markus Hipp •

Frank Kargl •Michael Weber

Received: 28 February 2011 / Accepted: 17 July 2011

ÓThe Author(s) 2012. This article is published with open access at Springerlink.com

Abstract Automotive navigation systems are becoming

ubiquitous as driver assistance systems. Vendors continu-

ously aim to enhance route guidance by adding new features

to their systems. However, we found in an analysis of current

navigation systems that many share interaction weaknesses,

which can damage the system’s credibility. Such issues are

most prevalent when selecting a route, deviating from the

route intentionally, or when systems react to dynamic traffic

warnings. In this work, we analyze the impact on credibility

and propose improved interaction mechanisms to enhance

perceived credibility of navigation systems. We improve

route selection and the integration of dynamic traffic warn-

ings by optimizing route comparability with relevance-based

information display. Further, we show how bidirectional

communication between driver and device can be enhanced

to achieve a better mapping between device behavior and

driver intention. We evaluated the proposed mechanisms in a

comparative user study and present results that confirm

positive effects on perceived credibility.

Keywords Automotive navigation systems ANS 

Credibility Automotive HMI HCI Navigation

1 Introduction

Automotive navigation systems (ANS) have matured into a

mainstream technology. While integrated ANS are mostly

found in middle- and higher-range cars, cheaper portable

navigation devices (PNDs) enable the addition of ANS into

any vehicle. A navigation system’s purpose is to support

drivers in traveling from location A to destination B with

route guidance. Modern ANS not only visualize the routing

process on maps but contain additional features, like text-

to-speech or advanced lane guidance with 3D visualization.

Many devices can also receive up-to-date traffic informa-

tion via the FM broadcast based TMC (Traffic Message

Channel) and similar services.

ANS can be seen as support systems for safety critical

situations, that is, the driving context. System errors or

confusing commands can have significant consequences in

cases where drivers rely blindly on the ANS. Especially in

unfamiliar environments, drivers place higher confidence

in navigation commands, while their self-confidence

decreases [12]. In such situations, gullibility errors may

occur, that is, the driver acts on an erroneous command

perceived as credible. If drivers experience erroneous or

misleading commands, they will trust ANS commands less

in the future. The credibility of the ANS is damaged as a

result, even due to small errors [8]. Credibility is a per-

ceived quality that reflects the trustworthiness and exper-

tise of a system. A loss of credibility in turn leads to

F. Schaub (&)M. Weber

Institute of Media Informatics, Ulm University,

89069 Ulm, Germany

e-mail: [email protected]

M. Weber

e-mail: [email protected]

M. Hipp

Institute of Databases and Information Systems,

Ulm University, 89069 Ulm, Germany

e-mail: [email protected]

F. Kargl

Institute of Distributed Systems, Ulm University,

89069 Ulm, Germany

e-mail: [email protected]

F. Kargl

DIES Research Group, University of Twente,

P.O. Box 217, 7500 AE Enschede, The Netherlands

123

Pers Ubiquit Comput

DOI 10.1007/s00779-012-0519-0

dismissal by the user. Therefore, exhibiting a high level of

credibility is important to ensure continuous use of the

system. High credibility ensures continuous benefit to the

driver, but is also economically relevant for the ANS

manufacturer, because low credibility may affect product

or brand reputation [8].

Credibility issues not only occur in unfamiliar but also

in familiar environments. In a familiar environment, a

driver may form her own belief of the best route in a given

situation. If the ANS does not support the driver’s intention

and does not make route recommendations sufficiently

comprehensible, credibility of the ANS will also suffer.

The driver may reject correct recommendations of the

ANS, known as incredulity errors [8].

Navigation systems produce different kinds of erroneous

messages that can impact credibility negatively. Many

issues can be traced back to weaknesses in usability and

interaction design. In this work, we provide an analysis of

interaction weaknesses in current ANS based on an

experimental study with PNDs (cf. Sect. 3) and relate them

to core issues impacting credibility (cf. Sect. 4). But first,

we provide background knowledge on credibility (cf. Sect.

2). We further propose a credibility-enhanced interaction

design for ANS (cf. Sect. 5) focused on common task

scenarios. Our evaluation in a comparative user study (cf.

Sect. 6) validates that our contributions improve ANS

credibility. We conclude the paper with an outlook on

future directions in this line of work (Sect. 7).

2 Background on credibility

Fogg and Tseng [8] define credibility as a perceived quality

comprised of a system’s trustworthiness and expertise.

Trustworthiness captures the perceived truthfulness of a

system. Expertise captures the system’s perceived knowl-

edge and capabilities. Note that credibility is mainly con-

cerned with believability, in contrast to trust that focuses

on dependability [8,26]. Fogg and Tseng [8] distinguish

four types of credibility. Presumed credibility based on

general assumptions about the system, for example, an

ANS should find a route from A to B. The perceived

quality of a system’s hardware and interface determines

surface credibility.Reputed credibility stems from experi-

ence reports by others, while experienced credibility results

from personal experience.

Fogg and Tseng [8] further distinguish four credibility

aspects users focus on when assessing credibility. Device

credibility relates to a system’s physical aspects, functional

credibility to its functionality. These aspects are deter-

mined by the casing and routing engine of an ANS.

Interface credibility and information credibility relate to

the interaction experience and to how believable

information given by the system is [18,26]. We mainly

focus on the enhancement of the latter two.

In general, systems can gain credibility with users when

they provide accurate information and lose it if provided

information or system behavior is perceived as erroneous.

Especially, small errors can have a disproportionally large

effect on perceived credibility [8,12]. Similarly, negative

experiences affect trust to a greater extend than positive

ones [16]. System credibility can be improved by facili-

tating understanding of system decisions [8,19] and

exhibiting reliable performance [16]. Interface design also

influences credibility [8,16]. For example, higher credi-

bility is perceived for esthetic websites [23], a factor that

could also be utilized by ANS. Too much trust, on the other

hand, leads to overreliance, that is, the user neglects crit-

ical assessment of system commands [20]. Situational

awareness is reduced and, thus, gullibility errors are

facilitated. Therefore, credibility improvements should

strive for an appropriate level of trust that matches the

system’s capabilities [16].

Most research on credibility focuses on website credi-

bility [7,18,22,23], only few work addresses credibility of

ANS. Kantowitz et al. [12] showed that unreliable traffic

information degrades the credibility of navigation systems.

Pauzie

´[21] mentions the ‘‘legibility and understandability

of messages’’ as a factor to gain benefits from ANS usage.

Ross and Burnett [24] point out that ‘‘trust issues’’ arise if

ANS directly start routing without showing the destination

or an overview map. In the field of automotive HMI, most

work focuses on general usability and interaction design

aspects of ANS under consideration of the driving con-

text’s special requirements [1,9,15]. Multimodality [13,

17,25] and driver attention and distraction [2,4,11] are

prominent research topics. Proposed concepts could also

positively affect credibility, for example, integrating

landmarks in navigation commands to establish consis-

tency with human navigational strategies [3]. However,

effects on credibility are often not specifically evaluated

and are not focus of related studies.

3 Analysis of current navigation systems

We assessed interaction weaknesses and credibility issues

of current ANS in an experimental study with PNDs [10].

We tested five PNDs from different manufactures in real

driving scenarios, ranging from low-end to top-range

models representative of the German PND market in late

2009. We chose PNDs to ensure comparability between

devices by mounting them in parallel in one car. We

devised different scenarios to study ANS behavior in nor-

mal operation and in situations where driver and device

intentions diverge. The first two scenarios simulate driving

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in unfamiliar environments, while the detour scenarios

simulate familiar environments:

Highway. The driver follows navigation commands on a

long stretch of highway, including a short break for

refueling.

Inner city. The driver is guided through a city center to a

previously selected destination, including search for

parking.

Detour (city). The driver takes a detour through the city

to stop for coffee, while the PNDs advise to take the

highway directly.

Detour (rural). The driver intentionally leaves the route

on the highway for spontaneous sightseeing via rural

roads.

Dynamic traffic warning. The integration of dynamic

traffic warning messages is tested by driving on routes

with reported traffic obstructions.

All devices were programmed with the same destination,

and it was verified that suggested routes matched. For each

scenario, audiovisual navigation commands of the devices

were recorded. The road situation was filmed to facilitate

correlation of navigation commands and driving situation,

later on. We categorized recorded commands along two

dimensions: message correctness (correct/false) and driver

anticipation (expected/unexpected). A command is defined

as correct if it corresponds to the driver’s intended route. For

example, if the driver leaves the route intentionally and the

ANS insists on turning around, these messages would be

considered false. As soon as the ANS recalculate and switch

over to an alternative route, which matches the driver’s

intended route, subsequent commands would be considered

correct again. Likewise, a message is expected if it can be

anticipated by the driver. It fits the current situation and

aligns with the driver’s behavior. Surprising messages that

do not fit the current situation appear unexpected. Note that

this does not mean that the driver knows the content of an

expected message in advance.

With these categories, we identified interaction weak-

nesses that cause mismatches between driver intention and

ANS behavior. Correct/expected messages are normal

navigation commands, and they constituted the majority of

observed messages in our study (56 %).

Correct/unex-

pected messages (1 %) are not anticipated by the driver, but

give correct advise. False/unexpected messages occurred

rarely (2 %), for example, an erroneous ‘‘turn around’’

command caused by GPS reception issues, while driving

with high speed on the highway. Such commands can cause

critical incidents in case of overreliance and potentially

high credibility loss. False/expected messages are small

errors that occur more frequently, for example, when

leaving the route. If acting intentionally, the driver can

expect these messages to be false. But persistent and

repetitive messages of this kind can become a nuisance and

reduce perceived expertise and trustworthiness. In our

study, we observed 40 % false/expected messages in the

detour (city) scenario, where the PNDs switched over to the

detour route once it became shorter, and 93 % in the detour

(rural) scenario, where no alternative route toward the

destination was available.

Based on the results, we identified three common situ-

ations with high false/expected rates that exhibited pre-

valent interaction weaknesses across tested devices [10]:

Route selection. When ANS propose a route, the criteria

for the recommendation are often unclear. The driver

receives insufficient information to validate system

recommendations. As a result, a mismatch between

the driver’s cognitive model of the best route and the

proposed one may occur. Diminished credibility is the

consequence.

Dynamic traffic information. When an ANS receives

information about traffic obstructions and proposes an

alternative route, provided information is often insuffi-

cient to make informed decisions. Choices were

restricted to accepting the alternative or staying on the

original route. Skepticism against these choices most

likely increases with higher familiarity of the environ-

ment. Thus, a credibility decrease can be expected

especially in familiar environments.

Deviation from route. ANS do not recognize if a driver

deviates from the route by mistake or intentionally, for

example, due to preferring familiar roads [14]. Thus, most

current ANS either try to direct the driver back to the

original route or recalculate the route to match the current

driving direction. The new route may match the driver’s

intention for the moment, but ultimately leads toward the

destination regardless of current driver intention. Incon-

sistent routing behavior with superfluous messages may

be the result and reduce system credibility.

While the study only assessed a small set of ANS, the

fact that the identified issues persisted across all tested

devices irrespective of price segment and manufacturer

suggests that the outlined interaction weaknesses deserve

closer attention.

4 Impacting credibility

Concerning credibility, the interaction weaknesses in the

identified scenarios can be broken down into a few core

issues. Insufficient information and insufficient choice are

We report only cumulative results as behavior differences were

marginal between devices. Please see [10] for detailed results for each

scenario.

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salient issues in the route selection and dynamic traffic

information scenarios. In the route deviation scenario, the

issue is neither the ANS trying to fulfill its routing goal nor

the driver intentionally ignoring navigation commands.

Insufficient communication capabilities are the problem.

The driver has no proper means to convey dynamic

intention changes to the ANS. In the following, we elab-

orate on these issues and their impact on credibility.

4.1 Insufficient information

Insufficient information reduces verifiability of system

decisions, which directly affects information credibility

and as how believable presented information is perceived.

When a mismatch between the driver’s model of the best

route and the system’s proposed route occurs, the infor-

mation is insufficient to convince the driver of the validity

of the system’s recommendation. If the driver cannot

comprehend the system’s actions, credibility is reduced.

Studies on website credibility have shown that addi-

tional information can enhance credibility [7] and that

perceived information credibility encourages users to fol-

low provided advice [18]. We hypothesize that the same

effect can be achieved for ANS by providing more infor-

mation about routing decisions. But due to the driving

context, information presentation must be unobtrusive.

Cognitive load has been shown to increase if drivers need

to decipher presented information [21]. Therefore, infor-

mation presentation must be optimized for the current sit-

uational context. ANS should provide the highest possible

amount of useful information as concise as possible (high

entropy, low bandwidth). Therefore, we propose a details

on demand approach. Only most relevant information

should be directly presented to the driver, with additional

information being available on demand. Current ANS

already provide information deemed relevant and even

selectively provide additional information, for example,

traffic message details, but information presentation is not

optimized to the situational context. A details on demand

approach tailored to the context would support the driver’s

assessment of the situation. This would help to resolve

mismatches between the driver’s and system’s route

models and, thus, retain credibility.

Another issue to consider is incorrect information in the

ANS, that is, information that is inconsistent with the

physical road situation, for example, road signs for one-

way streets or speed limits, which are not reflected in the

ANS. Optimized information presentation can help drivers

estimate the trustworthiness of system recommendations

[19,20] and calibrate their trust to an appropriate level. An

appropriate level of trust mitigates overreliance [16] and

should reduce the risk of gullibility errors from incorrect

information. In addition, other support systems such as

visual road sign detection can be employed for correction

of the ANS knowledge base.

4.2 Insufficient choice

The issue of insufficient choice is related to verifiability.

When explicit decisions are required, drivers need suffi-

cient information to validate the system’s recommendation

and evaluate alternatives. For example, based on provided

information, drivers must decide if they want to circumvent

a traffic jam or not. If the system does not properly support

this decision-making process, drivers cannot make an

informed decision (assuming they have no additional

information from other resources like radio traffic service).

If provided information is perceived as insufficient, drivers

may not believe that the recommended route is optimal and

the system will lose trust as a consequence [16]. If insuf-

ficient choices are offered, drivers will feel unsupported. In

both cases, the system’s interface credibility suffers. As

Fogg and Tseng [8] put it, ‘‘an interface is likely to be

perceived as less credible when it contradicts user expec-

tation or mental models.’’

Many current ANS do not support evaluation of alter-

natives well. At initial route selection, most ANS provide

only one route without alternatives [10]. In that case,

drivers can only influence route selection prior to route

calculation by setting few parameters, like fastest or most

economic route. Similarly, when reacting to dynamic

traffic warnings, the driver is commonly confronted with

one detour option, which she can accept or reject. While

some ANS manufacturers started offering multiple route

choices for initial route selection, its impact on credibility

has not been studied. We hypothesize that offering multiple

alternatives enhances credibility because drivers feel sup-

ported in the decision process and in control. Providing

more choice also means there are more options to properly

align the driver’s mental model and the system model,

which reduces the likelihood of mismatches between the

two. The system should preinterpret route alternatives to

obtain a relevancy-based ordering and provide an explicit

recommendation for the best route. The combination of

giving a clear recommendation and enabling comparison

with alternatives will likely suggest expertise to drivers.

4.3 Insufficient communication capabilities

ANS lack sufficient bidirectional communication capabil-

ities. With many current systems, drivers only act actively

when initially selecting the destination. While driving,

driver interaction is reduced to interface control, like

adjusting the zoom level or volume. Some inputs may not

be allowed at all while driving in order to minimize driver

distraction. Only occasionally ANS request driver input,

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for example, when showing a dynamic traffic warning.

Thus, while the system can convey dynamic information to

the driver, drivers have very limited ability to convey

dynamic intention changes to the system while driving.

Typical examples for dynamic intention changes would be

an unplanned trip to the grocery store or taking a detour for

sightseeing. Current ANS are unable to adapt dynamically

to such short-term changes in driver intention. Thus, when

the driver deviates from the route, either a route alternative

can be calculated or the driver is repeatedly asked to turn

around. In that case, the driver’s only options are to (1)

reprogram the route, (2) deactivate routing, or (3) switch

off the system. The first option entails an onerous process,

which could be dangerous while driving. The second

option often requires multiple steps and may only be per-

formed if sufficiently annoyed. The third option is quick

and effective, but the driver loses the moving map

functionality.

Due to the lack of bidirectional communication, a mis-

match between driver intention and system behavior

ensues. Both perceived expertise and trustworthiness are

likely damaged as a result, affecting functional and infor-

mation credibility. By providing bidirectional communi-

cation capabilities during driving, drivers would be able to

convey intention changes. Similar to how ANS prompt the

driver for input in specific situations, ANS should also

offer interaction capabilities to drivers in some situations,

for example, when detecting a route deviation. Such

interaction capabilities need to be unobtrusive and tailored

to the driving context, so that drivers can make use of them

if desired but are not forced to. By receiving explicit

intention input from drivers, false/expected messages could

be reduced, while retaining full functionality and utility. If

utility is retained, drivers continue to trust the system

because it performs as expected [16] and credibility

remains intact.

5 Credibility enhancing interaction design

A holistic approach for interaction design is required to

enhance ANS credibility. While improving credibility is

the goal, applied concepts must not substantially increase

driver distraction and cognitive load [1,9]. Navigation is,

and must remain, a secondary task in the driving context.

Thus, credibility guidelines from other domains, such as

website credibility [6,7,22,23], cannot be directly applied

to ANS. Following the discussion in the previous section,

we aim to enhance credibility by

1. Providing information relevant to the given situation

with additional information on demand to support

verifiability of system decisions,

2. Offering alternatives to involve the driver in decision

processes and to facilitate verifiability of system

recommendations, and

3. Improving bidirectional communication to either let

drivers convey intention changes when necessary or

engage them in interaction when intentions are

unclear.

In the following, we develop corresponding mechanisms

for the three scenarios previously identified: route selec-

tion, dynamic traffic information, and deviation from the

route. We will show that perceived credibility can be

enhanced by addressing the interaction weaknesses in these

scenarios with consistent improvements. Note that while

focusing on these scenarios in this work, we designed the

mechanisms with a broader task range in mind and are

convinced that they are applicable beyond the task sce-

narios covered here. We involved a small number of

drivers in the design process. They repeatedly provided

feedback on drafts and variants of specific mechanisms,

especially on the presentation of relevant information.

Suggestions were used as initial guidance for designing

interfaces with enhanced credibility.

5.1 Route selection

To enhance ANS credibility in the route selection process,

we offer three route alternatives to the driver, along with

information to facilitate comparison of these choices and

validation of the system’s recommendation. The driver

must explicitly select one of the routes to start routing. This

way, the driver should feel more involved in the route

selection, which supports trustworthiness [24]. Also, the

chance of routing errors due to wrongly selected destina-

tions is reduced, which would negatively impact experi-

enced credibility. We further encourage a validation of the

entered destination by labeling the routes with names of

characterizing streets as associative cues.

Figure 1a shows the route selection interface. The left-

most route is the system’s recommendation. The route

ordering provides a preference ranking that is additionally

reflected by color coding [27]. Salient route characteristics

are displayed to facilitate comparison. Route length,

duration, and traffic density enable micro-level route

comparison. Absolute and relative values are combined for

time and length to keep information presentation concise

and provide easily discernible tendencies. Traffic density is

indicated with a traffic light metaphor. If required, more

details can be accessed on demand with the respective info

button for each route. The star rating summarizes the

system’s recommendation by mapping the system’s inter-

nal ranking of each route to a star level. The star rating

supports macro level comparison. Within a glance, drivers

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can discern if the system rates all alternative routes similar

or if the recommended route is rated superior. The different

levels ease comparison while keeping cognitive load low.

This supports the driver’s understanding of the system’s

decisions and conveys expertise on multiple levels. Even if

drivers reject the recommended route and choose an

alternative, they should feel supported in their decision-

making process.

In addition, a map overview of all routes is available via

the Map button (cf. Fig. 1b). Coloring of routes is consis-

tent with the main screen. The map encourages validation

of the selected destination. The map centers on the current

position [5], but zooming allows to assess the complete

routes. A subset of route characteristics also enables

comparison. To enlarge map space, bar charts combining

absolute and relative values are used to compare route

duration and length. Information in the bar charts is

ordered in consistency with the main screen. The map view

facilitates spatial comparison, while the main screen is

optimized for multi-parameter comparison. By offering

choice also in terms of comparison views, different driver

preferences are supported.

5.2 Dynamic traffic information

The integration of dynamic traffic information is related to

route selection, but interaction occurs while driving.

Drivers may be alerted with an audio cue when the screen

appears. Drivers should be provided with meaningful and

verifiable choices when a traffic obstruction is reported. If

drivers are well supported in the decision-making process,

provided information will suggest expertise and trustwor-

thiness, while the decision itself (accepting or rejecting a

detour) should not affect the system’s credibility.

Consistent with the route selection scenario, three

choices are offered when a relevant traffic warning is

received: continue on the current route or select one of the

two route alternatives. Available information is distributed

between two screens to keep initial information presenta-

tion concise. The map view (cf. Fig. 2a) is the main screen

because spatial information is most useful to quickly

evaluate the extend of the obstruction and available

detours. Detailed information about the traffic obstruction

(cf. Fig. 2b) can be obtained via the more info button.

On the map, an icon indicates the obstruction and the

traffic jam is highlighted (purple). The rating of the choices

is conveyed with star ratings, ordering, and color coding,

analogous to the previous scenario. Bar charts are also used

here to facilitate comparison of length and duration. From

the bar chart, the potential time saving of alternative routes

is easily discernible. The reuse of familiar elements is

expected to keep cognitive load relatively low even if the

driver encounters dynamic traffic warnings rarely.

The details screen (cf. Fig. 2b) provides additional

information about the traffic obstruction if the driver wants

to validate the system’s recommendation. Its cause and the

message’s actuality are shown. The time estimate for

continuing is broken down into driving and waiting time to

facilitate understanding of the estimate’s nature. The

development of the traffic jam is visualized by a small

graph to give an intuition if it is increasing or decreasing.

The details screen is a map overlay, so that route selection

Fig. 1 Improved user interface

for route selection. aMultiple

routes on main screen,

bmultiple routes on map screen

Fig. 2 Improved user interface

for integrating dynamic traffic

information. aRoute with

obstruction and alternatives,

bdetails about traffic

obstruction

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buttons remain available. Thus, the driver can assess the

details and directly act upon them, which saves time and

should positively affect functional credibility.

5.3 Deviation from route

Deviations from the route can be unintended or intended by

the driver, but current ANS always assume an unintended

deviation and cannot handle intended behavior. The result

is potentially annoying turn around messages. However,

supportive routing is essential in the case of unintended

deviations. We propose an interaction design that supports

both unintentional and intentional deviations by enabling

bidirectional communication.

When a deviation is detected, our system informs driv-

ers that they left the route with one concise voice com-

mand. The interface in Fig. 3a enables the driver to

explicitly convey whether the deviation was intentional or

unintentional by continuing or pausing navigation. Unless

the driver reacts, the system assumes that the deviation was

unintentional and continues routing. Thus, unintentionally

acting drivers are not impaired by the dialog. After 15 s,

the popup disappears to restore map visibility. The driver’s

inactivity is interpreted as implicit input, which is mapped

to an unintentional deviation. However, the driver still has

the option to pause routing with a button on the map (cf.

Fig. 3b). If navigation is paused in either dialog, the ANS

switch to free drive mode, that is, the current position is

shown on the map without further routing commands. The

button in Fig. 3b changes to continue and routing can be

resumed anytime. The proposed interaction flow allows the

driver to confirm intentional deviations while still fully

supporting the driver in case of unintentional deviations,

with the result that false/expected messages are eliminated

and functional credibility is enhanced.

6 Evaluation

To evaluate the impact on ANS credibility, we imple-

mented the developed concepts in a prototype system and

conducted a comparative user study.

6.1 Setup and scenario description

An experimental group (EG) tested our prototype as the

experimental system (ES), while a control group (CG) used

acontrol system (CS). CS was consistently modeled after

interaction and interface concepts representative of PNDs

used in our original analysis [10]. The graphical design of

CS and ES was homogenized to eliminate potential biases.

To ensure intra- and intergroup comparability of results,

we opted for a desk-based laboratory study. ES/CS

behavior was predefined and consistent for all participants.

This allowed us to eliminate biases potentially caused by

divergent routing behavior and driving variations in driving

simulations and solely focus on the assessment of ANS

credibility. We synchronized ES and CS actions to a

recorded video of an actual drive, which was shown to

participants while interacting with the ANS in the three

scenarios. The simulated trip was 6.5 km long, consisting

of 4.7 km rural and 1.8 km urban roads. Because partici-

pants were not actively driving, a disconnected task was

introduced to generate a basic level of cognitive load.

Arrows appeared shortly above the video and participants

had to press respective arrow keys within a 5-s time frame.

After each completed scenario, the test was interrupted and

participants were presented with a questionnaire.

The route selection scenario was performed before

driving started. Participants were asked to start routing to a

destination. The destination was already prefilled and

participants had to press plan route. ES presented the route

selection (cf. Sect. 5.1) while CS started navigation directly

on the ‘‘fastest route.’’ In both cases, the same driving

video was shown because route alternatives diverged only

later on.

Next, the evaluation of the dynamic traffic information

scenario followed in the rural section of the trip. Partici-

pants were allowed to practice the disconnected task for a

few minutes without ANS interaction. Then, an accident

report appeared and participants had to decide whether to

circumvent the obstruction or not. CS provided a map with

one detour option and duration/length information only for

the detour, while ES offered multiple choices and details

on demand (cf. Sect. 5.2). For both systems, continuing on

Fig. 3 Improved user interface

for deviations from route.

aPause routing dialog, bnon-

modal pause dialog

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the route was the fastest option. The immediate driving

situation was not affected by the user choice as the accident

was further ahead on the route.

In the urban section, the deviation from route scenario

was evaluated. Participants were told that they forgot to

buy something and should turn right to reach the grocery

store. The simulated trip deviated accordingly from the

ANS route. CS routed back to the original route with

voice commands, while ES offered the pause function (cf.

Sect. 5.3).

6.2 Metrics

Credibility and related concepts are perceived qualities,

which we assessed with a questionnaire after each sce-

nario. Participants were asked to directly rate perceived

credibility. But because credibility is a rather intangible

concept, we also employed related terms suggested by

Fogg and Tseng [8] to indirectly assess credibility. We

asked for the perceived believability and reliability to

measure trustworthiness and functional credibility. Thus,

it is expected that results for believability and reliability

also reflect effects on credibility. We further asked par-

ticipants to rate the perceived ability to influence system

decisions (influence) to support the assessment of credi-

bility. Further items addressed experienced mental

workload (mental_load) and asked to rate the amount of

provided information (info_amount). Items were formu-

lated as assertions, and participants were asked to rate

them on a 5-point Likert scale from does not apply at all

(1) to applies fully (5). Items concerning the amount of

cognitive load or information could be rated from too

less (1) to too much (5). Questionnaires were identical

for both groups, except for additional items for EG to

assess bar charts and star rating elements only present

in ES.

6.3 Participants

The study was conducted with 42 participants (25

female, 17 male), equally distributed between groups.

Participants were recruited from the university’s student

body, mainly from the disciplines Computer Science,

Psychology, and Biology. All participants were aged

18–30, with the majority 25 or younger. Participants

were randomly assigned to EG or CG. The demographic

items age,gender,technical affinity, and ANS ownership

were used as control variables. The distribution of

technical affinity differentiated significantly between

groups (two-tailed ttest) and was, therefore, applied as

covariant in all variance tests to compensate for non-

uniform distribution.

6.4 Results for route selection scenario

Variance analysis results for route selection are summa-

rized in Table 1. While the difference in directly perceived

credibility is not significant, believability was rated sig-

nificantly higher by EG (p=.026), which can be inter-

preted as an indicator for higher credibility. The ability to

influence the system’s decisions was perceived signifi-

cantly higher in EG (pB.001). Mental load was low in

both groups, but the amount of available information was

rated significantly higher by EG (p=.003). Thus, it can be

concluded that the information in ES was more relevant to

drivers. This is further supported by results for ES’ addi-

tional interface elements. The star rating was perceived

helpful (EG ¼4:09;r¼:94) and comprehensible

(EG ¼4:52;r¼:75) while creating only low cognitive

load (EG ¼1:48;r¼:75). The bar charts were considered

comprehensible for time (EG ¼4:05;r¼:89) and length

comparison (EG ¼4:15;r¼:88). They also positively

impact believability of system recommendations, with time

charts having a higher impact (EG ¼4:15;r¼:75) than

length charts (EG ¼4:10;r¼:64). We conclude that

facilitating comparability likely improves ANS credibility,

because system recommendations are easier to validate.

6.5 Results for dynamic traffic information scenario

Table 2summarizes the results. Credibility was perceived

higher by EG, and believability was significantly higher

(p=. 017). Concerning the influence of available choices

on believability (choice_bel), no difference between

offering one (CS) or two alternatives (ES) was found. But

considering that believability and the information amount

were rated significantly higher by EG, it can be concluded

that information has higher influence on believability than

choice alone. This is further supported by the observation

that only 38.1 % of CG chose to stay on the route (faster

choice), in contrast to 61.9 % of EG; 57.1 % of EG used the

details view, and 83.3 % of those chose to continue. This

shows that providing additional information on demand

Table 1 Results for the route selection scenario

Characteristic CG rCG EG rEG p

Credibility 4.38 .80 4.48 .75 .490

Believability* 4.10 .70 4.57 .51 .026

Reliability 4.00 .89 4.34 .74 .160

Mental_load 1.29 .56 1.57 .68 .162

Info_amount* 2.76 .54 3.29 .56 .003

Influence** 2.33 1.24 3.95 1.16 \.001

* = 5 % significance level; ** = 0.1 % significance level

Pers Ubiquit Comput

123

leads to better informed decisions, which translate to less

frustration and higher experienced credibility. At the same

time, the details on demand approach did not increase

perceived cognitive load.

The ES’ traffic jam characteristic (cf. Fig. 2b) was found

to increase believability ðEG ¼4:47;r¼:74Þ. It was rated

highly comprehensible ðEG ¼4:73;r¼:79Þ, while creat-

ing only low cognitive load ðEG ¼1:67;r¼:98Þ. The star

rating received results similar to the previous scenario.

Results for bar charts are also comparable, but slightly

below the results of the previous scenario.

6.6 Results for deviation from route scenario

Table 3summarizes results. While results show no sig-

nificant difference for credibility, reliability was signifi-

cantly better in EG (p=.023). As expected, CG rated the

ability to influence the system quite low and stated that

issued voice commands increased cognitive load (acous-

tic). Both items were rated significantly better by EG.

Thus, the pause function significantly enhances the per-

ceived reliability and, therefore, credibility. Furthermore,

the CG results underline the negative effect of false/

expected messages.

6.7 Combined scales

The results for perceived characteristics such as credibility

and believability do not exhibit consistent significance across

scenarios. This instability is likely caused by their subjective

nature. We presumed the subjectivity issue and, therefore,

measured not just credibility but also the named related

concepts that enabled some inferences for credibility.

In order to analyze general effects of the measured

characteristics independent of specific scenarios, we com-

bined items from all scenarios that measure one charac-

teristic into a combined scale. For example, all items on

credibility from the three scenarios were combined in one

scale. This way, combined scales for believability, reli-

ability, and influence were formed. Table 4gives results of

the variance analysis on combined scales. All measured

characteristics have been estimated significantly higher by

the EG in the combined scales. Credibility of the improved

system was perceived consistently higher in all scenarios,

the combined credibility scale confirms this at a significant

level. Note, however, that values for Cronbach’s aare

slightly lower than typically expected (a\.7), which

indicates a not fully consistent scale. A reason could be that

participants might have attributed slightly different notions

to credibility and related concepts across scenarios.

7 Conclusions

In this paper, we proposed three major concepts for ANS

interaction design: (1) providing choices when decisions

are required, (2) providing relevant information that facil-

itates comparison of alternatives, and (3) enabling bidi-

rectional communication to let drivers convey intention. As

a result, system decisions are easier verifiable, drivers feel

involved and supported in navigation-related decisions, and

false/expected navigation commands can be reduced.

The user study validates the positive effect on credibility

of these concepts. However, it also shows that credibility is

difficult to measure reliably. Assessment of related termi-

nology is necessary, as already suggested by Fogg and

Tseng [8], but also affects result reliability. The laboratory

study provided unified conditions across groups, which

simplified comparative evaluation of the developed con-

cepts. At the same time, it introduced certain limitations.

The actual risk of bad decisions usually experienced while

Table 2 Results for the dynamic traffic information scenario

Characteristic CG rCG EG rEG p

Credibility 3.43 1.47 4.19 .93 .055

Believability* 3.38 1.28 4.24 .77 .017

Reliability 3.48 1.25 4.14 .79 .450

Mental_load 2.71 1.10 2.71 1.19 .563

Info_amount* 2.67 1.06 3.52 .68 .017

Influence 3.52 1.47 4.19 1.21 .127

Choice_bel 3.95 1.12 3.71 1.52 .309

* = 5 % significance level; ** = 0.1 % significance level

Table 3 Results for the deviation from route scenario

Characteristic CG rCG EG rEG p

Credibility 4.10 1.09 4.57 .60 .101

Believability 3.71 1.19 4.29 .96 .078

Reliability* 3.62 1.28 4.33 .86 .023

Mental_load 2.81 1.03 2.19 1.29 .281

Info_amount 2.95 .92 3.05 .38 .474

Influence** 2.00 1.18 4.33 1.24 \.001

Acoustic* 3.38 1.56 2.00 1.18 .002

* = 5 % significance level; ** = 0.1 % significance level

Table 4 Combined scales

Characteristic CG EG Fp a

Credibility* 3.81 4.33 59.67 .029 .655

Believability** 3.73 4.37 68.08 \.001 .649

Reliability* 3.70 4.29 68.46 .010 .618

Influence** 2.62 4.16 24.01 \.001 .842

Pers Ubiquit Comput

123

driving is missing, but may further impact credibility. The

disconnected task did create basic cognitive load, as all

participants were highly engaged in completing the task

properly, but measurements did not allow statements about

actual driver distraction. Especially, the influence of

additional information on driver distraction requires further

attention. Currently, the proposed concepts have only been

assessed for three scenarios, and more studies under real

driving conditions are required with ANS that fully

implement these concepts to obtain more reliable results on

their overall effect on ANS credibility. Long-term driver

studies could also provide insights on how credibility and

cognitive load develop over time. Comparing the impact on

credibility of navigation commands and human driving

suggestions could also provide interesting results.

The proposed concepts mainly improve credibility by

enhancing explicit interaction. In future work, we plan to

investigate the effects of implicit interaction on ANS

credibility. Driving itself can be considered an implicit

input channel, which allows inference of driving habits and

potentially intention. ANS already contain sensors to

measure location, heading, and speed to inform the navi-

gation process. These parameters could be monitored over

time to infer driving patterns and context. Furthermore,

in-vehicle sensors for breaks, engine management, indica-

tors, or steering could enrich context information. Implicit

input could enhance credibility by optimizing system

adaptation to the current context and tailoring explicit

interaction accordingly. As one benefit, explicit interaction

could be shifted to moments of relatively low cognitive

load. For example, when the driver indicates while waiting

at a traffic light although the route continues straight, the

system could inquire the driver’s intentions even before a

deviation occurs. As another benefit, implicit interaction

could be used to provide personalized route recommenda-

tions based on prior behavior. Future work is required to

assess the potential and limitations of implicit interaction

and potential benefits for ANS credibility.

Acknowledgments This work was supported by Transregional

Collaborative Research Centre SFB/TRR 62 (‘‘Companion-Technol-

ogy for Cognitive Technical Systems’’) funded by the German

Research Foundation (DFG). The authors would like to thank Roger

Walk and Tina Seufert for their support and fruitful discussions, as

well as the anonymous reviewers for their valuable feedback.

Open Access This article is distributed under the terms of the

Creative Commons Attribution License which permits any use, dis-

tribution, and reproduction in any medium, provided the original

author(s) and the source are credited.

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