# Beyond the Dashboard:
Investigating Distracted Driver
Communication Preferences for ADAS

Aamir Hasan1, D. Livingston McPherson1, Melissa Miles2, Katherine Driggs-
Campbell1 Support for this work was provided by State Farm.1A. Hasan, D. L.
McPherson, and K. Driggs-Campbell are with the Department of Electrical and
Computer Engineering at the University of Illinios at Urbana-Champaign.
Emails: {aamirh2, dlivm<EMAIL_ADDRESS>Miles is with State Farm,
Bloomington IL. Email<EMAIL_ADDRESS>
###### Abstract

Distracted driving is a major cause of road fatalities. With improvements in
driver (in)attention detection, these distracted situations can be caught
early to alert drivers and improve road safety and comfort. However, drivers
may have differing preferences for the modes of such communication based on
the driving scenario and their current distraction state. To this end, we
present a user study (N=147) where videos of simulated driving scenarios were
utilized to learn drivers’ preferences for modes of communication and their
evolution with the drivers’ changing attention. The survey queried
participants preferred modes of communication for scenarios such as collisions
or stagnation at a green light. We validate our hypotheses and provide key
results that inform the future of communication between drivers and their
vehicles. We showcase the different driver preferences based on the nature of
the driving scenario and also show that they evolve as the drivers’
distraction state changes. All materials associated with the study can be
found at https://sites.google.com/illinois.edu/driver-preference-for-modes.

###### Index Terms:

Advanced Driver Assistant Systems (ADAS), Modes of Communication, User Study,
Driver Preferences, Autonomous Vehicles, Adaptive Systems

††publicationid: pubid: 0000–0000/00$00.00 © 2021 IEEE

##  I Introduction

The United States National Highway Traffic Safety Administration reported that
9.8% of road fatalities occurred due to distracted and drowsy drivers in 2021
[1]. This statistic, not including the number of non-fatal cases, could have
been reduced through distraction detection. Recent advances have improved
driver (in)attention detection by use of methods involving gaze estimation,
EEGs, and heart rate monitoring in driver monitoring systems [2, 3, 4, 5]. As
distraction detection improves due to these advances, alerting drivers and/or
integrating systems for automatic takeovers (i.e. the transfer of control of
the vehicle between autonomy and the driver) to prevent such catastrophes
becomes paramount in reducing fatalities and other road accidents. Though
commercial integration of takeover technologies (e.g. lane keeping assist) has
begun with Advanced Driver Assistance Systems (ADAS), drivers find the
interventions confusing and uncomfortable without prior explanations [6].
Moreover, most drivers prefer interactive takeover requests over automatic
takeovers [7]. Thus, the paradigm for communication for alerts and interactive
sessions between drivers and their ADAS is of utmost importance for safe and
comfortable driving.

Through improving distraction detection and communication technologies, car
manufacturers have introduced safety alerts for drivers who are detected to be
distracted or drowsy by the driver monitoring system (e.g. Subaru [8]). As
these features are only available in select cars (often expensive), the pool
for user feedback that the designers receive is severely limited. These models
also use the same modes of communication (i.e. the modality of communicating
with driver such as icons and simple beeps) regardless of the drivers
abilities to perceive them or their experience using ADAS [8]. This system has
led to an increase in over-reliance and decrease in safety. For example,
drowsy drivers will often ignore indicators from their cars due to over-
reliance [9]. Thus, there is a lack of concrete understanding of driver
sentiments and preferences regarding the modes of communication with current
solutions.

Therefore, the development and effectiveness of safety alert systems can be
significantly improved with a more comprehensive understanding of human-
vehicle communication under distraction. While standards for the modes of
communication have been established [10, 11], they follow the one mode for all
scenarios paradigm. Such a paradigm is impractical as driver psychologies and
reaction times have been shown to change under differing (in)attention states
[12]. Thus, the questions of if and how driver communication preferences
change based on their distraction state is important. Motivated by these
questions, we aim to understand the effects of driver distraction on their
communication preferences. In particular, we study how driver preferences for
the mode of communication changes under different distraction levels and the
risk associated with the driving task.

The inferences gained from studying these preferences can then be used to
develop an Adaptive Communication Module (AdaCoM) that could alleviate the
above problems by autonomously switching the mode of communication based on
the driver’s distraction state. Such an idea for human-vehicle interaction
(HVI) has existed for decades but has yet to come to fruition due to
unreliable perception and the complexities of the human-automata team [13,
14]. The development of these systems becomes more plausible and inevitable as
the capabilities in detecting driver distraction improve. While adaptive ADAS
such as Adaptive Cruise Control exist, they adapt to factors outside the
vehicle and are easily set by the driver. Additionally, without proper user
feedback, the framework for driver-ADAS interaction suffers and leads to
driver over-reliance and discomfort. Thus, there is a lack of understanding
driver preferences for HVI and in-vehicle adaptive communication systems.

Figure 3: Example clips from videos shown in the user survey. Each video is
divided into two sections. The top section of the video shows the front view
through the windshield from the drivers seat. The bottom section shows the
left side view mirror, the rear view mirror, and the right side view mirror
from left to right. Video (b) and (c) are not augmented and present all
information about the scenario to the participants. Video (a) is augmented to
introduce a hyper-fixation distraction.

To address these gaps, we conducted a comprehensive user study111The full
survey and videos are linked on the project webpage:
https://sites.google.com/illinois.edu/driver-preference-for-modes (N=147) to
study drivers’ mode preferences and their evolution. We also study driver
sentiments towards AdaCoM systems to motivate their development. We first
learn drivers’ preferred modes of communication by way of questionnaires on
videos of simulated "real world" driving scenarios. The videos, as shown in
Figure 3, were designed to test if drivers preferences depended on the risk
associated with the driving scenario and their attention. The driving
scenarios depicted in the videos ranged from pedestrian and car collisions to
lane deviation and stagnation at green lights. We also asked drivers about
their perceived comfort and trust with an adaptive communication module. We
analyze the survey responses to show that drivers have different preferences
for modes of communication based on the risk involved in the driving scenario.
We also verify that drivers’ preferences vary as their distraction state does
and that they would want an adaptive communication module in their vehicles.

Our main contributions are as follows:

  1. 1.

We conduct a user study with 147 participants with diverse experiences to test
four hypotheses on how HVI preferences evolve with changing distraction
states.

  2. 2.

We showcase key results and recommendations regarding the use of different
modes of communication based on the driving scenario and the drivers
distraction state.

  3. 3.

We offer insights on driver sentiments towards an adaptive communication
module for driver-vehicle interaction.

To the best of our knowledge, this is the first user study to directly
investigate driver preferences for the modes of communication with ADAS in
everyday driving scenarios. This paper is organized as follows. Section II
introduces related work. Section III summarizes our hypotheses. We then
describe our formal study in Section IV. The results of the user study are
presented in Section V. Finally, we conclude the paper and discuss future
works and limitations in Section VI.

##  II Related Work

We provide a brief introduction to the different modes of communication used
in human=vehicle communication in this section. We also provide an overview of
previous work in adaptive human-automation interaction.

###  II-A Modalities in Human-Vehicle Communication

The three main modes of communication in Human-Vehicle teams are: (1) Visual;
(2) Auditory; and (3) Haptic. Visual displays such as touchscreens and heads-
up displays have been used extensively as technology has matured to display
non-emergent information [10]. Researchers have focused on the different
methods to design these visual interfaces and how their layouts affect
drivers’ cognitive load [15, 16, 17]. Conversely, auditory modes such as
beeps, ultrasonic bursts, and natural language are recommended for emergent
alerts [10, 11, 18]. Haptic modes in different areas of the vehicle such as
the steering wheel, seat, and even the gas pedal are also suggested for
emergent alerts [19, 20, 21]. Additionally, researchers have aimed to capture
the effect of different kinds of auditory signals such as beeps and
conversational natural language on drivers cognitive loads [22, 23], and the
improved efficiency of non-driving related tasks with tactile modes [24].
Works have also tested the affect of combinations of multiple modes such as
speech with visuals [20] and gestures on the steering wheel [25],
respectively, and found that the use of the combinations reduced the total
cognitive load on the driver.

Even though distracted driving is a major factor affecting road safety, it is
often ignored by prior work in human-vehicle communication that instead
focuses on the cognitive load placed on the drivers due to different modes as
shown above. We address this gap by querying user preferences for the modes of
communication under different driver distraction levels. A recent work by Du
et al. aims at addressing driver preferences for modes of communication,
particularly the different combinations of visual and auditory modes [26].
This work by Du et al. investigates driver preferences under different display
modes, types of information, and event criticality. While their work primarily
focuses on takeover scenarios, our work is generalized to non-takeover
scenarios. Moreover, we also factor in drivers attention level and its affect
on their mode preferences. Additionally, we do not restrict ourselves to
visual and auditory modes and include other modes such as tactile modes and
different types of visual and auditory modes that are omitted from their work.
However, unlike Du et al., we do not consider or test the advantages of
combining different modes of communication.

###  II-B Adaptive Human-Automation Interaction

Human-automation adaption is a major field of study at the intersection
Psychology and Automotive Sciences. Adaptive systems could benefit human
performance if the interaction between the human-automation team was adapted
to their task and contextual demands [13]. These adaptive systems would
decrease the cognitive load on drivers leading to an improved driving
experience [27]. Such adaptive systems would also vastly improve driver
engagement as humans tend to lose interest in the task at hand if automation
does not adapt quickly [28]. Other works have modeled human-automation
interaction with the use of Hidden Markov Models and Markov Decision Processes
[29, 30, 31]. These models are trained using simulated data but are yet to be
tested on real world systems.

Other works have focused on adapting ADAS to human preferences. Personalizing
ADAS has been popular and proposed to improve driving experiences [32, 14]. In
particular, Boverie et al. propose DrivEasy, a framework for adaptive ADAS
that behaves similarly to AdaCoM systems [14]. While DrivEasy provides an
overview for such an adaptive ADAS framework, no information about the design
of adaptive communication is provided. Additionally, driver sentiments towards
such a system and its adaptive communication are not highlighted. In our work,
we directly address this gap by testing driver comfort and perceived safety
with such AdaCoM systems.

Figure 4: An overview of the survey: Participants first fill out a
demographics questionnaire, followed by a driving experience questionnaire.
Then, participants are randomly assigned one of three tracks that contain
distinct videos showcasing the 5 different driving scenarios under different
environmental conditions and augmentations. Finally, the survey ends with a
questionnaire on their need for and comfort with a system with adaptive
communication.

##  III Hypotheses

In this work, we investigate driver preferences for the mode of communication
with the ADAS. Our four hypothesis are:

  1. H1

Driver preferences for the mode of communication are based on the hazardous
nature of the driving scenario. We treat this hypothesis as a confirmation
hypothesis as the association of modes of communication with their perception
by drivers is standardised by multiple transportation authorities [10, 11].

  2. H2

Drivers change their preferred mode of communication as their distraction
state changes.

  3. H3

Drivers prefer that their car autonomously adapt its mode of communication to
their changing distraction state.

  4. H4

Vehicles that implement an adaptive policy for its mode of communication are
more trusted by drivers.

##  IV Method

In this section, we describe the procedure and contents of our user study. We
first present an overview of the user study. Then, we explain the rationale
behind the design of the videos and our survey questionnaires. This study was
approved by the institutional review board at the authors’ institute.

###  IV-A Procedure Overview

The study11footnotemark: 1 was administered online through Google Forms.
Participants were recruited through Amazon MTurk, public social media channels
such as Reddit and LinkedIn, and email. The use of these channels allowed for
a wider variance in participant experiences and backgrounds. Only participants
from Amazon MTurk were compensated with $12 per hour. While the participation
of the unpaid respondents was voluntary and due to their interest in the
subject matter, we observed no significant differences between the responses
of the paid vs unpaid participants.

Participants in the study followed the timeline shown in Figure 4. To begin
the study, participants were asked to fill an informed consent form. After
submitting the form, they were directed to a demographics questionnaire and a
driving experience questionnaire222Details regarding the demographics and
driving experience questionnaires are omitted from this paper but can be
viewed on the project webpage11footnotemark: 1.. Only participants who were
above the age of 18, resided in the United States, and held a drivers license
were allowed to proceed further. Participants were then randomly assigned one
of three video tracks. Each video track consisted of distinct videos and a
questionnaire for the scenario shown in each video. Further details regarding
the videos and the questionnaires are presented in Section IV-B. Participants
were allowed to view the videos multiple times before answering the
questionnaire. Following the video track, participants filled out a
questionnaire regarding their opinion on an adaptive communication module for
vehicles.

Note that as with similar user studies that were conducted completely online
[33, 34], there was no participant oversight by researchers. While responses
were scrubbed to exclude faulty responses via completion time analysis (e.g.
if participants finished the whole survey very quickly) and response pattern
recognition (e.g. if participants provided the same response for all videos),
the effect of other factors such as external distractions is not included.
After sanitation, 147 user responses were obtained. As a consequence of this
nature of the survey, the results presented below must be verified thoroughly
through naturalistic studies.

###  IV-B Videos and Modes of Communication Questionnaire

TABLE I: The detailed video sequences for each track. We show the driving
scenario, weather conditions, and if the video was augmented. The slight
dominance of scenarios depicting rain in Track 3 was completely incidental.

Video # | Track 1 |  | Track 2 |  | Track 3
---|---|---|---|---|---
Scenario | Weather | Augmented |  | Scenario | Weather | Augmented |  | Scenario | Weather | Augmented
1 | Blind Spot | Night | ✓ |  | Car Collision | Clear | ✓ |  | Blind Spot | Rain | ✓
2 | Car Collision | Night | ✓ |  | Blind Spot | Clear | ✓ |  | Car Collision | Rain | ✓
3 | Still at Green Light | Clear | ✓ |  | Still at Green Light | Rain | ✓ |  | Lane Deviation | Clear | ✓
4 | Lane Deviation | Rain | ✓ |  | Lane Deviation | Night | ✓ |  | Still at Green Light | Night | ✓
5 | Pedestrian Collision | Clear | ✓ |  | Pedestrian Collision | Night | ✓ |  | Pedestrian Collision | Rain | ✓
6 | Still at Green Light | Clear |  |  | Car Collision | Clear |  |  | Blind Spot | Rain |
7 | Pedestrian Collision | Clear |  |  | Still at Green Light | Rain |  |  | Still at Green Light | Night |
8 | Car Collision | Night |  |  | Pedestrian Collision | Night |  |  | Car Collision | Rain |
9 | Blind Spot | Night |  |  | Blind Spot | Clear |  |  | Lane Deviation | Clear |
10 | Lane Deviation | Rain |  |  | Lane Deviation | Night |  |  | Pedestrian Collision | Rain |

In order to test H1 and H2, participants were shown videos from a driver’s
perspective simulated in the CARLA Driving Simulator [35]. All videos can be
viewed on the project webpage11footnotemark: 1. The videos mimic realistic,
high-risk scenarios, which could not safely be collected in a real vehicle. As
seen in Figure 3, the layout of the videos was designed to show the front view
through the windshield, and both side views and the rear view through their
respective mirrors.

Each video included one of five driving scenarios at varying levels of risk.
The driving scenarios, in order of decreasing risk were: (1) Pedestrian
Collisions; (2) Car Collisions; (3) Lane Deviation; (4) Blind Spot
Obstruction; (5) Stagnant at a Green Light. The five scenarios were selected
due to the ubiquity of ADAS developed to address them (e.g. collision
detectors, lane keeping assist systems). The weather in the videos varied
between: (a) clear day; (b) rainy day; and (c) night. In total, 15 videos ($3$
weather conditions $\times$ $5$ scenarios) were produced for every combination
of driving scenario and weather condition stated above. The number of
pedestrians and vehicles and, the environment setting (urban center or
highways) were also varied across each video i.e. each video is unique with
overlaps in only the type of scenario and the weather condition.

Environmental variations and other agents in the simulation were used to
introduce realistic distractions. Particularly, multiple video editing
techniques extrapolated from prior work focusing on driver inattention were
used to simulate distractions such as: (1) drowsiness; (2) hyper-focus on the
road; and (3) noticeable agents in the environment. These distractions were
chosen due to their occurrence in driver distraction literature and the ease
of implementing them in a simulated environment. Drowsiness was simulated by
continually splicing the video with a plain black screen with an increasing
duration and frequency to mimic an increase in blinking rate and the change in
perception associated with drowsy driving [36]. Inspired by existing
literature, we used editing techniques such as zooming in, cropping out, and
selectively blurring areas on the videos to simulate hyper-focus on areas in
the environment [37]. We also used other dynamic agents in the environment
such as children, emergency vehicles, and pedestrians with unique behaviors as
sources of distraction outside vehicles. The videos were edited to blur out
all views except the aforementioned elements and/or dynamic agents of
interest.

Each of the 15 videos were augmented in only one of the styles described above
to produce 15 pairs of videos ($3$ weather conditions $\times$ $5$ scenarios
$\times$ 2 versions). Therefore, a total of 30 videos were produced and
ordered in each of the three tracks. The 15 pairs of videos were divided into
three tracks such that each driving scenario appeared exactly once in every
track with no overlapping videos across tracks. In particular, each track
comprised of 5 videos covering each driving scenario and their augmented
versions. All augmented versions of videos appeared before the unedited videos
(see Table I). This ordering reduced bias by ensuring that participants did
not have a complete view of the scenarios in the augmented versions that they
could obtain in the original unedited videos. The videos were also distributed
as to have maximum coverage in all tracks for the differing weather conditions
and distraction augmentations. Therefore, each scenario was viewed and
evaluated independently by every participant.

The validity of the original videos and their augmented versions was
qualitatively verified in a small pilot study (N=4). Participants in the pilot
study were shown the videos and asked to describe what driving scenario and
distraction (for augmented videos only) was simulated in the videos.

TABLE II: The Options for the Modes of Communication

Modes of communication
---
Non-flashing icon on dashboard/central console screen | Flashing icon on dashboard/central console screen
Flashing icon on left/side view mirrors and/or rear-view mirrors | Spoken language alert (e.g. Siri)
Sound alert (e.g. Single beep) | Repeating sound alert (e.g. Multiple beeps)
Vibrations on steering wheel | Vibrations in Driver’s seat
Text alert on dashboard/central console screen | Image on dashboard/central console screen

After carefully watching each video by imagining themselves as the driver of
the vehicle, the participants were asked to answer a mode preference
questionnaire. The questionnaire involved ranking the modes of communication
listed in Table II and indicating if their vehicle should automatically
takeover control. The modes in Table II were selected due to their popular use
in vehicles and prior literature [10]. Participants were required to choose
only their top three preferred modes for the scenario taking place in every
video shown. A Likert scale was used to gather responses for sentiments
regarding automatic takeovers for each video.

The questionnaire was designed to obtain the drivers’ mode preferences across
"real world" scenarios with differing risk and under distractions. To minimize
the effects of individual biases, inferences from the videos were extrapolated
using a within-subjects strategy. For this portion of the study, the dependent
variables were the ranks associated with each mode and the independent
variables were attributes associated with each video i.e. the driving
scenario, the weather conditions, and if and how the video was augmented.

We posit that H1 holds true if the there is a change in the number of
selections for the modes across the different driving scenarios. Similarly, we
posit that H2 holds true if there is a difference between the order of the
preferred modes (i.e. rankings of the modes), for the original and augmented
versions of the videos per participant. We believe that these tests are robust
due to the considerations in the design of the video tracks. Further details
regarding the evaluation are discussed in Section V.

###  IV-C Adaptive Communication Mode Questionnaire

Following the video questionnaire, participants were given the following
description of an Adaptive Communication Module for Driver Assistive Systems
along with an example:

> An Adaptive Communication Module for Driver Assistive Systems can be defined
> as technology that learns and adapts the mode of communication of
> information to be relayed by Assistive Systems to the changes in the
> driver’s psycho-physiological attributes while taking into consideration the
> environment around the driver (and their vehicle) as well as the driver’s
> communication preferences.

To test H3, participants were asked if they would feel safer in a car with
such a system over a car without such a system as both a driver and a
passenger. We posit that drivers’ perceived sense of safety indicates a
preference for a system as we assume that most rational drivers value safety.
The dependent variable was how safe participants felt, and the independent
variables were the demographic details of the participants333Discussed in
Appendix -C..

Implementing an Adaptive Communication Module would require in-car driver
monitoring to assess drivers current distraction state. Currently, gaze based
distraction estimation is a state-of-the-art non-invasive method to estimate
driver distraction which only requires eye gaze data through the use of
ubiquitous low-cost RGBD cameras [2, 3, 38]. While, other methods of
estimating driver distraction such as EEGs and heart rate monitors are more
accurate and have been used in previous naturalistic user studies, they are
more invasive and require additional complicated hardware [39, 40, 5].
Additionally, the general population is also unfamiliar with these devices and
their inclusion would require additional context which would make the survey
more complex and arduous. Therefore, participants were asked if they would be
comfortable with their audio and facial videos being recorded by their
vehicle. The dependent variable was how comfortable participants felt, and the
independent variables were the demographic details of the participants. We
verify this claim by asking participants about their distractions in the
driving experience questionnaire444Results are presented in the Appendix -D..
We posit that the user’s comfort with a plausible detection system in addition
to their perceived sense of safety indicates their trust in such a system to
test H4.

###  IV-D Participants

Of the 147 participants, 90 participants were recruited through Amazon MTurk,
while the rest were recruited through the other channels mentioned Section
IV-A. Three participants were excluded from the study as they did not meet the
inclusion criteria. Therefore, a total of 144 participants (mean age=37;
median age=39; range=18-65; 87 males; 57 females) were considered in the
analysis presented in Section V.

On average, participants reported 19 $\pm$ 13 years of driving experience, and
having spent 5 $\pm$ 5 hours and 2 $\pm$ 4 hours driving and as a passenger
each week, respectively. 76% of the participants reported being at least
somewhat familiar with the development of ADAS and/or Autonomous Vehicles. 72%
of the participants reported having at least a college diploma. Participants
were split 37-37-26 along the three video tracks. The uneven split of the
participants across the tracks was attributed to the self-random selection
process. Furthermore, a video response is complete if all three preferred
modes and the preferred takeover option are selected. Only complete responses
all videos were considered for analysis. In total, 1408 complete responses
were analyzed.

##  V Results and Discussion

In this section, we analyze the responses obtained from our user study. We
present qualitative and quantitative results to support our hypotheses. We
also derive conclusions regarding the usage of modes based on the driving
scenario. Additionally, we recommend design changes for the communication
paradigms between drivers and ADAS.

###  V-A The relation between the modes and characteristics of a driving
scenario

Figure 5: The distribution of the modes of communication based on the
hazardous nature of the driving scenario (left to right in decreasing order),
regardless of distraction status. Each section of the vertical bars shows the
number of times the mode was picked as the first (bottom), second (middle),
and third (top) choice respectively.

Multiple statistical tests were performed to compare the effect of the
different driving scenarios (independent variable) on the ranks assigned to
the modes (dependent variable): (1) A one-way ANOVA revealed that there was a
statistically significant difference between the mode rankings for the five
driving scenarios with F-score of $28.19$ with $p<0.05$ on average for all
modes; (2) A Chi-square Goodness of Fit test was performed to determine
whether the weighted mode selection frequencies (calculated as $3\times
f_{\text{rank 1}}+2\times f_{\text{rank 2}}+1\times f_{\text{rank 3}}$, where
$f_{\text{rank $i$}}$ is the frequency of the mode selected at rank $i$) was
equal between the different driving scenarios. The test revealed that the mode
frequencies differ by the driving scenario with $\chi^{2}(3,5)=158.85,\
p<0.001$ on average for all modes. Other Chi-square tests that were used to
determine whether the frequencies of the modes at each rank was different
between each driving scenario also indicated similar results. Both statistics
discussed above indicate a statistically significant difference in mode
frequencies between the different driving scenarios.

As seen in Figure 5, participants prefer different modes of communication
depending on the hazardous nature of the driving scenario. Particularly,
participants prefer direct modes that can be interpreted quickly such as
repeated sound alerts and vibrations for emergent, dangerous scenarios. For
low-risk scenarios, participants prefer modes that serve as reminders and can
be interpreted without haste, such as a singular sound beep or spoken language
alerts. These conclusions are in line with the guidelines presented by
Campbell et al. for HVI as well as the results presented by Du et al. [10,
26].

Meanwhile, some modes such as non-flashing icons and text or images on the
dashboard are not preferred by participants in any setting. These modes are
consistently the least selected modes across all driving scenarios presented
in the study. Notably, these modes are usually reserved for long term
information regarding vehicle health and might be unsuitable for conveying
information for the driving scenarios depicted in the study. However, a
growing number of vehicles implement text and image alerts on the dashboard
and central consoles as common modes for all types of automotive information
[41]. We suggest that these modes should be used sparsely and with careful
consideration.

The difference between the frequency of the modes for the most and least
hazardous scenario: Pedestrian collisions and Standing still at a Green light,
clearly indicates that drivers prefer different modes of communication based
on the risk involved in a scenario. This conclusion is also reaffirmed by the
steady change of the frequency of modes as the risk in the scenario decreases,
as seen from left to right in Figure 5. However, we note that the final
rankings of the different modes is consistent across the first four driving
scenarios. While this consistency indicates an overall unchanging sentiment,
we note that the deviations in the mode frequencies for each scenario are
significant as proven by the one-way ANOVA test. Moreover, there are
significant deviations in the frequency of selections for each mode that imply
the importance of personalization for an adaptive communication framework.
Therefore, we conclude that H1 is true. We also note that our findings are
consistent with that of Du et al. [26]. This confirmation of H1 provides
credence to the results presented below as H1 was intended as a confirmation
hypothesis.

###  V-B Evolution of driver preferences with changing attention

The responses from the video questionnaires can be treated as a ranking of the
top-3 preferences out of the 10 choices presented in Table II. These top-3
rankings can be transformed to fully ranked lists of all the modes by allowing
for ties. We choose to assign all modes that were not selected by users a tied
rank of 4. As mentioned in Section IV-B, we posit that H2 holds true if the
ranked lists for the non-augmented and augmented versions of the videos are
not fully correlated. We use the following rank correlation metrics to compare
the responses for each video pair:

  1. 1.

Kendall’s $\tau$ [42]: The difference between the fraction of ranking pairs
that appear in order and the fraction of ranking pairs that appear out of
order. Values close to 1 indicate strong agreement while values close to -1
indicate strong disagreement.

  2. 2.

Spearman’s $\rho$ [43]: The Pearson correlation coefficient between the rank
variables. Values close to 1 indicate agreement while values close to -1
indicate disagreement.

  3. 3.

Rank Biased Overlap (RBO) [44]: The expected average overlap between the two
rankings at incremental overlap depths. Values close to 1 indicate agreement,
while values close to 0 indicates disagreement.

The different metrics measure different types of correlation between the two
rankings. Both Kendall’s $\tau$ and Spearman’s $\rho$ are unweighted measures
and only check to see the ordering of the two rankings is the same without
placing any weight on the change in ranking (i.e. scores for items with rank 1
and 2 would be the same as the score for items with rank 2 and 3). Whereas,
the RBO metric assigns significance to the actual rank changes and thus weighs
the changes in ranking more than just their order. Therefore, RBO is more
sensitive to changes in ranking. All analysis was carried out in python using
the scipy and rbo packages [45, 46].

TABLE III: The Correlations Between the Ranked Modes of Communication for the
Non-augmented and Augmented Videos. The p-values for all metrics was less than
0.0004.

Scenario | Kendall’s $\tau$ | Spearman’s $\rho$ | Rank Biased Overlap
---|---|---|---
Pedestrian Collision | 0.684 $\pm$ 0.337 | 0.714 $\pm$ 0.341 | 0.708 $\pm$ 0.289
Car Collision | 0.607 $\pm$ 0.338 | 0.642 $\pm$ 0.343 | 0.655 $\pm$ 0.292
Lane Deviation | 0.566 $\pm$ 0.372 | 0.600 $\pm$ 0.384 | 0.621 $\pm$ 0.305
Blind Spot Obstruction | 0.512 $\pm$ 0.378 | 0.544 $\pm$ 0.389 | 0.569 $\pm$ 0.316
Still at a Green Light | 0.479 $\pm$ 0.396 | 0.512 $\pm$ 0.418 | 0.543 $\pm$ 0.315
| 0.569 $\pm$ 0.372 | 0.603 $\pm$ 0.383 | 0.619 $\pm$ 0.309

Table III shows the correlations for these metrics aggregated according to the
driving scenario. The correlations were aggregated as simple averages and the
p-values were aggregated using the Fisher exact test [47]. All metrics show
that the rankings for the non-augmented and augmented versions are not fully
correlated but only slightly positively correlated. We speculate that this
result is a consequence of the modes of communication being more closely
related to the critical nature of the driving scenario than the current
distraction state. We further conjecture that the positive correlation implies
that while driver preferences for the mode of communication change, drivers
are not completely changing their minds on what modes best suit the current
driving scenario. We also observe that the correlation between the rankings
decreases as the scenarios become less hazardous. The correlation values
ranging from 0.5 to 0.7 reinforces this belief. Additionally, we believe that
these values are also a result of converting the partially ranked top-3 list
to a fully ranked list with ties at rank 4. This policy ensures that a large
portion of the ranking lists are the same, since 7 out of 10 options were all
ranked at the same rank. This procedure heavily biases the correlation
observed between the rankings to be positive. Based on these findings we
conclude that H2 is true.

###  V-C Sentiments towards an Adaptive Communication Module

Figure 6: The distribution of participant responses for perceived safety in a
vehicle with an Adaptive Communication Module as a driver (top) and passenger
(bottom). Figure 7: The distribution of participant responses for their
comfort with audio (top) and visual (bottom) recordings in their vehicles.

The responses for participants perceived safety in a vehicle with an adaptive
communication module are shown in Figure 6. The mean score (1=Strongly
disagree; 5=Strongly agree) for perceived safety as a driver and passenger are
$3.93\pm 0.91$ and $3.95\pm 1.03$, respectively. Over 80% and 77% of
respondents believe that they would feel safer in a vehicle with AdaCoM as a
driver and as a passenger, respectively. No bias between demographic variables
for the participants and their perceived safety was indicated by model fitting
using an Ordinal Multinomial Probit model. Therefore, we conclude that H3
holds true. However, we note that this finding is based on responses obtained
from asking participants about a hypothetical system. While the results
presented here motivate the need for development and testing for such a
system, there is no strong conclusion regarding its reception.

The responses for the participants comfort with a plausible implementation of
an AdaCoM system are shown in Figure 7. The mean score for comfort with audio
and video recordings are $2.32\pm 1.20$ and $2.14\pm 1.19$, respectively. Over
60% and 67% of respondents are uncomfortable having their audio and video
recorded by their vehicles, respectively. Model fitting using an Ordinal
Multinomial Probit model showed that the age of the participants was a
statistically significant $(p=0.056)$ indicator of the participants
(dis)comfort. Particularly, younger participants were more likely to be
uncomfortable with their videos recorded when compared to older participants.

While this reluctance indicates distrust in the system, a conclusion about H4
cannot be reached. We speculate that this distrust is largely due to the
conjectural nature of the questions. The general population is conscious of
their data privacy and hence distrustful of companies (mis)using their data.
The questions asked in this context were phrased in a manner that did not
specify what the collected recordings would be used for and how that data
would be handled. This ambiguous phrasing could have led to participants
conflating recent trends in data privacy and their concerns with the intended
purpose of the question. These results along with the results shown in Figure
6 indicate that while drivers would prefer such a system, they are unwilling
to accommodate its easiest plausible implementation. As such, we cannot offer
a conclusion regarding H4.

##  VI Conclusion and Future Work

In this paper, we present a user study that aims at testing driver preferences
for the modes of communication between drivers and their assistant systems. We
conducted a online user study (N=147) to test our four hypotheses regarding
how drivers prefer their assistant systems communicate with them. Our user
study involved participants viewing a set of videos and answering
questionnaires about the videos, as well as questionnaires about an adaptive
communication module. We validate our hypotheses that driver preferences for
the modes of communication depend on the critical nature of the driving
scenario and their attention state. We also affirm that drivers would feel
safer with and hence prefer an adaptive communication module in their
vehicles. Lastly, we hypothesised that drivers would trust vehicles that
autonomously adapted their mode of communication, but found inconclusive
evidence for confirmation.

###  VI-A Limitations

While our work can provide automotive designers with important guidelines for
ADAS development, it has the following limitations. Firstly, our study was
conducted online where researchers had no interaction with participants and
hence had to rely on metrics such as completion time and validation questions
to verify responses. Additionally, researchers were unable to control the
participants environment during the study and hence could not ensure minimal
distractions. Though the videos shown were made of the highest possible
resolution, compression on the hosting website as well as the quality of the
participants internet connection at the time of completing the survey could
have severely hindered the quality of the videos. This reduction in quality
could lead to participants missing key details in the videos that could impact
their responses. In general, even though we gained important insights into
driver behavior through the study, we cannot provide concrete recommendations
without further naturalistic studies, which we leave for future work.
Secondly, two of the four hypotheses tested relied on participants
understanding of a hypothetical system that they have no prior experience
with. Therefore, any extrapolations made from the results presented in this
paper should be thoroughly verified and tested independently. Lastly, the
videos shown as part of the survey were recorded in a driving simulator and
hence can only represent real world driving up to a reasonable level of
accuracy.

###  VI-B Future Work

Though the work presented here has the above limitations, the results
discussed lead to following interesting avenues for future work. The results
for H1 and H2 provide a basis for vehicle manufacturers to revisit traditional
modes of communication for critical information in vehicles. In particular,
the blind spot icon on mirrors is currently a popular mode for presenting
information about the vehicles blind spot [10]. The results presented in this
work show that other modes of communication or combinations of multiple modes
might be preferred for that purpose. Conversely, modes of communication that
rely on non-flashing icons and natural language text on the dashboard are not
popular choices for most scenarios tested. However, recent designs for
vehicles show an increase in the use of such modes [41]. Additional in-car
studies to test the utility of these modes would provide further clarity into
the most efficient ways of communicating important information to drivers.
Finally, the development and testing of an adaptive communication module would
greatly help extend the frontier of driver-automation communication due to the
confirmation of H3. These adaptive systems for communication will also aid in
providing a more comfortable driving experience and improve overall road
safety.

## Acknowledgements

Support for this work was provided by State Farm. The authors would like to
thank Dr. Roy Dong, Dr. Peter Du, Neeloy Chakraborty, Ye-ji Mun, and Jerrick
Liu for their support and feedback. The authors would also like to thank all
the participants in the survey for their participation.

Figure 8: Participant responses for the frequency with which they perform non-
driving related activities while driving.

###  -C Demographics Questionnaire

Participants were asked demographics questions to determine how their personal
characteristics influenced the responses to other questions in the survey.
These questions were asked to determine the following characteristics: Age,
Gender, Race, Level of Education.

###  -D Driving Experience Questionnaire

We also asked participants about their driving experience to factor in the
influence of their driving styles on the other questions in the survey. The
questions asked determined the following factors: Driving experience in years,
Time spent driving every week, on average, Time spent as a passenger every
week, on average, Familiarity with the development of Autonomous Vehicles
and/or ADAS.

We also asked participants about the frequency with which they drive with the
following distractions: Alcohol, Recreations Drugs, Sleepiness/Drowsiness,
Text messages, Phone calls, Video calls, Secondary activities such as applying
make up, eating, etc, Passengers, Highway hypnosis.

The aggregated responses for the frequency with which participants perform
non-driving related activities are shown in Figure 8. Overall, participants
attempt to keep their sources of distraction at a minimum. Taking phone calls
while driving is reported as the most plausible source of distraction, with
drowsiness and other passengers in the vehicle following suit. This order
reinforces our prior belief about the sources of distraction and how to
capture them within cars using audio-visual recordings as discussed in Section
IV-C.

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Aamir Hasan Aamir Hasan is a PhD candidate in the Electrical and Computer
Engineering Department at the University of Illinois Urbana-Champaign. He
received his BS in Computer Engineering from UIUC in 2019. His research
interests lie in applying machine learning for human-autonomy interaction,
particularly in modeling and improving communication between drivers and their
(smart) vehicles.
---
D. Livingston McPherson Livingston will be starting as a teaching assistant
professor in the Department of Computer Science at the University of Illinois
at Urbana-Champaign in 2024. Prior to that, they worked as a postdoctoral
scholar in the Department of Electrical and Computer Engineering at UIUC,
where they worked on combining research in cognitive science with robotics to
design collaborative and transparent automation. They completed their doctoral
research on statistical and control-theoretic models for human safety
judgements with Shankar Sastry at UC Berkeley in 2022. Now they are extending
their study of active understanding to manufacturing, robots for
accessibility, and computer education.
---
Melissa Miles Melissa Miles received the BS in Chemistry from Illinois State
University. She is currently a Research Program Administrator at State Farm.
Her research focuses on driver behavior, road user safety, advanced vehicle
technology, telematics, and the driving environment.
---
Katherine Driggs-Campbell Katherine Driggs-Campbell received the BSE in
electrical engineering from Arizona State University and the M.S. and Ph.D.
degrees in electrical engineering and computer science from the University of
California, Berkeley, CA, USA. She is currently an Assistant Professor with
the ECE Department at the University of Illinois at Urbana-Champaign, Urbana,
IL, USA. Her research focuses on exploring and uncovering structure in complex
human-robot systems to create more intelligent, interactive autonomy. She
draws from the fields of optimization, learning & AI, and control theory,
applied to human robot interaction and autonomous vehicles.
---