quote

# Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards
Real-Time Advisory Systems from a Driving Simulator User Study

Aamir Hasan†, Neeloy Chakraborty†, Haonan Chen†, Cathy Wu‡, and Katherine
Driggs-Campbell† †A. Hasan, N. Chakraborty, H. Chen, and K. Driggs-Campbell
are with the Department of Electrical and Computer Engineering at the
University of Illinois Urbana-Champaign. Emails: {aamirh2, neeloyc2, haonan2,
<EMAIL_ADDRESS>Wu is with the MIT Laboratory for Information & Decision
Systems (LIDS), the Department of Civil and Environmental Engineering (CEE),
and the Institute for Data, Systems, & Society (IDSS) at the Massachusetts
Institute of Technology, Email<EMAIL_ADDRESS>received ; revised .

###### Abstract

Real-time Advisory (RTA) systems, such as navigational and eco-driving
assistants, are becoming increasingly ubiquitous in vehicles due to their
benefits for users and society. Until autonomous vehicles mature, such
advisory systems will continue to expand their ability to cooperate with
drivers, enabling safer and more eco-friendly driving practices while
improving user experience. However, the interactions between these systems and
drivers have not been studied extensively. To this end, we conduct a driving
simulator study (N=16) to capture driver reactions to a Cooperative RTA
system. Through a case study with a congestion mitigation assistant, we
qualitatively analyze the sentiments of drivers towards advisory systems and
discuss driver preferences for various aspects of the interaction. We comment
on how the advice should be communicated, the effects of the advice on driver
trust, and how drivers adapt to the system. We present recommendations to
inform the future design of Cooperative RTA systems.

###### Index Terms:

Advanced Driver Assistant Systems (ADAS), Qualitative Analysis, Driving
Simulator, Case Study, Cooperative Real-time Advisory Systems, Driver
Preferences

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

##  I Introduction

Drivers everywhere rely on Real-time Advisory (RTA) systems almost every day,
popularly in the forms of navigational [1, 2, 3, 4] and eco-driving assistants
[5, 6, 7, 8, 9, 10]. With the improvements in intelligent driving systems, new
RTA systems are continually proposed to aid in driving scenarios such as
controlled takeovers [11, 12, 13, 14] and safe decision making [15, 16, 17,
18]. These RTA systems are deployed inside vehicles through onboard systems
developed by car manufacturers in the form of Advanced Driver Assistant
Systems (ADAS) at Society of Automotive Engineers (SAE) level 2 autonomy [19],
or through the use of smartphones [20, 21]. With increasing demand for such
RTA systems, Autonomous Vehicles (AV) pose as the natural solution to
encapsulate issues that these systems address. However, the market penetration
of AVs is likely to be delayed due to safety risks [22, 23] and economic
factors [24, 25, 26], particularly in developing countries with large driving
populations [27]. Thus, the development of these systems for such underserved
markets, usually through SAE level 2 systems, can have a significant impact on
_our_ future.

Figure 1: An illustration of real-time advisory systems helping drivers. The
CoRTA system above observes drivers and their environment to provide advice to
the driver and improve their user experience.

These new developments continue to increase the time granularity of how
frequently RTA systems advise drivers – ranging from landmark-based events in
navigation and turning assistants [3, 4] to second-by-second advice in eco-
driving and speed advisory assistants [28, 29, 30]. As this time granularity
increases, the interactions between these systems and drivers must be
formulated with careful consideration. One avenue of improving user
experiences with such time-precise systems is through the inclusion of
cooperative features [31, 32, 33], resulting in Cooperative Real-time Advisory
(CoRTA) systems (illustrated in Figure 1). Such CoRTA systems are cognizant of
driver behavior and preferences, and thus allow for enhanced interactions with
drivers. These interactions are likely to not only inform user experiences,
but also to impact driver trust in automation, and hence, influence the
efficiency of downstream Human-Vehicle teaming tasks (e.g. eco-driving) [34,
31].

The influence of AV behaviors on driver-vehicle interactions are widely
studied at the SAE Level 3 or higher. Particularly, the impact of different AV
behaviors on driver attitudes (e.g. trust, comfort) towards these systems has
been extensively researched, to inform future designs using driving simulator
studies [35, 33, 36], semi-structured interviews [37, 34], and meta-surveys
[38, 31]. For example, a driving simulator study to capture the effects of
assertive AV takeover maneuvers (i.e. the transfer of control of the vehicle
between automation and the driver) by Kraut et al. [35] found that such
behaviors invoked a faster response time in drivers without influencing driver
stress or safety. Similarly, Helldin et al. [36] found an inverse effect of
providing uncertainty information on driver trust in AVs during shared-control
driving.

While such evaluations are abundant for systems with SAE Level 3 and above,
similar treatments of driver attitudes for SAE Level 2 autonomy, particularly
CoRTA systems, are sparse. Works that do address these gaps are informative
for future designs of CoRTA systems but recommend further studies on
understanding how the system behaviors influence driver trust and reliance
[39]. Some works only evaluate singular factors such as the User Interface
(UI) [11, 40], while others primarily aim to test the effectiveness of the
systems [8, 30].

Considering solely eco-driving CoRTA systems, previous works have used
different evaluation approaches. Some works forgo human-in-the-loop testing
and rely completely on software simulations, either due to limiting
assumptions that render them hard to test, or unsatisfactory performance in
non-idealized settings [41, 42, 7, 30]. Others such as Birrell et al. [8] test
their solutions in naturalistic studies, but only discuss the efficiency of
their methods and its impact on user behavior without evaluating driver
preferences on the interactions. Meanwhile, driving simulator studies that
address driver preferences have been conducted but only evaluate certain
aspects of the UI. For example, Shahab et al. [28] test auditory modes to
communicate how drivers should change their speeds based on pitch and Di Lena
et al. [10] test the function of a _gamified_ UI. Both works successfully show
the validity of their UIs, but do not discuss driver preferences for either
the systems’ behaviors or the advice provided by such systems. On the other
hand, Chada et al. [5] capture quantitative metrics on the potential impact
and user acceptance of their novel system [6], but do not discuss qualitative
opinions of drivers. Moreover, new learning-based eco-driving CoRTA systems
that could significantly improve the efficiency of downstream tasks [43, 44,
45, 46, 47, 48, 49, 30, 50] are untested with humans. Therefore, their
interactions with users have not been studied extensively. Thus, the following
questions for interactions between drivers and (learned) CoRTA systems require
more attention:

  1. RQ1

What are driver expectations for the behavior of CoRTA systems?

  2. RQ2

How do Driver-CoRTA systems interactions affect driver behavior, trust in
automation, and user experience?

  3. RQ3

What are the UI requirements desired by drivers in their interactions with
CoRTA systems?

In this article, we seek to address these questions in capturing driver
sentiments towards CoRTA systems to enable better cooperation by combining a
driving simulator study with semi-structured interviews. Particularly, we
first conduct a human-in-the-loop driving simulator study to allow
participants to directly interact with CoRTA systems that exhibit a variety of
behaviors. Then, we conduct semi-structured interviews to qualitatively
understand the impact of the diverse behaviors on drivers and their
perceptions to capture user preferences for their interactions with CoRTA
systems. We analyze the sentiments expressed by drivers and discuss how their
interactions can alter driver behavior. Particularly, participants in our case
study interacted with 9 CoRTA congestion mitigation assistants in the form of
eco-speed recommending Piecewise Constant Policies [49] (described in Section
II-A) and provided their firsthand sentiments on the interaction via semi-
structured interviews. These recommendations and findings are relayed in this
article to impact the design and development of future CoRTA systems.

Thus, our main contributions are:

  1. 1.

A driving simulator user study (N=16), focused on young adults, that captures
their sentiments towards CoRTA systems through a case study on congestion
mitigation assistants.

  2. 2.

A qualitative analysis of driver preferences to provide insights into driver
interactions with CoRTA systems.

  3. 3.

Recommendations in designing future CoRTA assistants.

To the best of our knowledge, we are the first to qualitatively investigate
the broad sentiments held by drivers towards CoRTA systems through our user
study.

##  II Method

In this section, we first provide a short summary of the policies used in our
CoRTA systems. We also introduce our user study, its procedure, and the user
interface employed for the user study.

###  II-A The Advisory Policies

As shown in Figure 1, CoRTA systems are designed to provide a driver with some
advice on how they should act in real-time while adapting to drivers’ style
and situational environmental factors. Piecewise Constant (PC) Policies [49]
exemplify such cooperative systems to address congestion mitigation, i.e. the
reduction or dampening of stop-and-go waves in traffic [51, 48]. PC policies
are policies trained using reinforcement learning that require the current
action, i.e. the advice, to be held constant for a period of time – referred
to as the hold-length, $\delta$. Sridhar and Wu [49] designed this hold period
to account for the time taken by drivers to react to and act upon the advice,
and hence enabled the policies to be cooperative. Residual variants [30] of
such policies enable further cooperative behaviors through the use of driver
trait inference modules. These policies aim to advise drivers of the speed
that should be maintained in order to mitigate congestion.

In our case study, we use PC policies and two of their residual
variants11footnotemark: 1 that observe the driver and their environment to
provide speed advice (See Appendix B for details). Particularly, we utilize 9
different policies: 3 policy types $\times$ 3 different hold-lengths:
$\delta=\\{5,7,10\\}$ seconds. The policies exhibited diverse behaviors in the
speed advice they recommend and how the speed advice changes in real-time. For
example, the advised speeds range between slow, “just right”, or fast speeds,
and there are either frequent or no changes, and even incremental or large
changes in the advised speed. Some policies advise a sequence of speeds that
when acted upon by drivers are intended to amount to a different, more
effective speed advice i.e. such policies are not direct in communicating
their advised speeds. Other policies are transparent and advise drivers on the
most effective speed advice for their task. While some systems may be more
effective in mitigating congestion than others, all systems were cooperative
and provided real-time assistance. The analysis provided in this article does
not focus on the individual differences in the policies, but on the effects of
their diverse behaviors on their interactions with drivers. For the remainder
of this article, we use the phrases _policy_ and _system_ interchangeably. We
also use the phrases _advice_ and _speed_ interchangeably.

###  II-B The Simulation Environment

The simulated environment (shown in Figure 2) consists of $40$ vehicles
driving on a single lane circular ring road with inner circumference of $628m$
and outer circumference of $654m$ in the CARLA Simulator [52]. Only one
vehicle was controlled by the participant, while the other 39 vehicles were
controlled using the IDM car following model [53]. The weather in the
simulation was set to a clear sunny day across all trials. No other agents
(other than the 40 vehicles) were present in the simulation to ensure minimal
distractions to participants. This simulation setup is designed to be
consistent with the congestion mitigation literature [51, 48, 43].

Figure 2: The simulation environment: A ring network with 40 vehicles, where
one vehicle is controlled by the participant.

Figure 3: The driving simulator setup.

(a) Driving outside the advised range.

(b) Driving inside the advised range.

Figure 4: The user interface to convey the advice from the policy to the
driver.

###  II-C The User Interface

The participants’ viewpoint is from the driver’s seat, which includes the
front view through the windshield and the rear-view mirror. The speedometer is
shown at the bottom of the screen. Figure 4 shows the user interface for the
study where the advice from the policies is communicated to the drivers via
the speedometer, similar to other studies providing real-time speed advice [9,
40]. The speedometer shows the speed of the vehicle in the form of a gauge and
a number at the center of the gauge in a large font. We display the advised
speed on the speedometer as a red line surrounded by a $\pm 1m/s$ ($\sim 2.5$
mph) green range. Participants were told that the red line indicated the exact
speed that the policy was advising, but that they should try to keep their
speed within the green range. The green range effectively acted as an
acceptable error bar for the drivers. The number on the speedometer was
highlighted in green when the participant’s speed was within the acceptable
error bar (See Figure 4(b)).

###  II-D The Procedure

Figure 5 illustrates an overview of the procedure for the user study. We
conducted a 3-person pilot study before recruitment to validate the study
procedure. To begin the study, participants were first introduced to the goal
of the study and briefed about their task. Participants were told that the aim
of the study was to test congestion mitigation assistants and capture driver
sentiments towards interactions with the assistants. Additionally,
participants were also told that their task was to (1) drive safely (avoid
collisions, stay on-road, etc.) and (2) follow the advice provided by the
system. All participants were explicitly instructed to ignore the advice of
the policy if they determined that it would lead to unsafe driving or a loss
in control of the vehicle. The study was conducted in a controlled lab
environment and actions were taken to ensure that there were no external
distractions present during the study procedures.

Figure 5: An overview of the user study procedure with the expected timeline
for one participant.

Participants first signed a consent form and then answered a pre-study
questionnaire consisting of questions on demographics and driving experience.
Participants were then given test runs to get familiar with using the driving
simulator, the user interface, and interacting with the system. Test runs were
conducted in the same manner as the trials, until participants self-reported
being comfortable controlling the vehicle111Only 2 participants required more
than 2 test runs, all other participants only required 1 test run.. The policy
used during the test runs was not evaluated during trials to prevent any
biases. Subsequently, 9 different policies were tested in 5-minute trials,
with 5-minute breaks every 3 trials. The order of testing the 9 policies was
uniquely randomized for each participant. During each trial, participants
first controlled their vehicle without any advice to enable the formation of
stop-and-go waves. After 90 seconds had elapsed, the policy began advising the
participants for the next 200 seconds. A trial was repeated if collisions
occurred or if participants drove off the road222An average of 0.875
collisions and 0 off-roading related restarts occurred for each participant.
After each trial, participants answered a post-trial
questionnaire22footnotemark: 2 that included the NASA Task Load Index (TLX)
Questionnaire [54], the System Usability Scale (SUS) Questionnaire [55], and a
question on how likely the participant was to use the policy in the real
world. Following the post-trial questionnaire, a short semi-structured
interview was conducted where the participants were queried about their
experience during the trial. At the end of all 9 trials, another semi-
structured interview was conducted to gauge participants’ views on the user
interface and the systems as a whole. In total, each participant was involved
in the study for at most 90 minutes. All interviews were audio recorded and
transcripts of the recordings were analyzed after the end of the study.

###  II-E The Participants

The IRB approved study was conducted in a between-subjects manner where all
policies were tested by all participants in a unique, random order. Our study
population consisted of N=16 participants (10 male, 6 female) who were
recruited using flyers and emails at the authors’ university. No compensation
was offered for participation in the user study. We acknowledge that
participants are likely to have been enthusiastic about driving simulators as
a factor of our recruitment and compensation policy. Participants had a mean
age of $23.7\pm 2.8$ years where all were at minimum high school graduates or
had earned their GED certificate or equivalent, with mean driving experience
of $5.5\pm 2.5$ years. Participants were also informed not to discuss the
study with anybody except researchers to avoid inadvertently inflicting biases
on future participants.

##  III Results and Discussion

TABLE I: A summary of our recommendations for the design of future CoRTA systems, particularly real-time speed advisory systems. A more detailed table can be found in the Appendix as Table III | Rec. | Behavior/attribute | Rec. | Behavior/attribute
---|---|---|---|---
Advice | ✓ |  _sweet spot_ † speeds | ✗ |  Extreme (too slow† or too fast†) speeds
✓ | Smooth, gradual† changes in advice | ✗ |  Large† change in magnitude
✗ | Frequent or sudden change in advice | ✗ | No change in advice
✗ |  Oscillations between values in a set | ✓ |  Transparent and direct
✓ | Matches user’s driving style† | ✓ | Reflects current traffic state
✓ | Easy to understand | ✓ |  Adapts to driver’s past history†
User Interface | $\sim$ | Speedometer | ✓ |  Visible in peripheral vision
✓ |  Use of colors† to indicate changes | ✓ |  Comfortable† Acceptable Error bar
✓ Recommended ✗ Not Recommended $\sim$ Controversial † Personalized

In this section, we provide our conclusions on the _do’s_ and _don’ts_ for
CoRTA systems, that are summarized in Table I. All participant responses were
edited by a research assistant to remove filler words (e.g. ‘like’, ‘umm’) to
shorten responses that were verbose or repetitive. Then, thematic coding was
performed using NVivo14 on the responses by a single coder to seek sentiments
relevant to our research questions. After all responses had been coded,
resulting themes and significant quotes were identified and used to construct
the analysis presented here. To enhance quote understanding we replace
verbiage and add context (between [brackets]) for some responses. We attest
that the context and sentiments of participants are presented as they were
intended. Note that we refer to each participant as P## where ## is the number
assigned to them between 1 and 16.

###  III-A “What did you think of the advice?"

> “I think it’s the best one… It seemed like the suggested speeds were very
> accurate, like if I did them, nothing bad would be happening." - P11

Our interviews revealed multiple behaviors and characteristics of real-time
advisory systems that significantly affected participant experiences.
Unsurprisingly, the most significant attribute of the advice that drivers seek
is accuracy (as it pertains to them). Although in the quote above, P11
described accuracy as the ability of the policy to keep them safe, other
factors such as the value of the advised speeds, the frequency of speed
changes, the magnitude of the changes, time period to react to advice, and the
understandability of the advice were all important contributors. These
behaviors affected drivers’ trust in the system, and led to drivers adapting
their driving style, and, in some extremes, ignoring the advice completely.

The Value of the Advice: Users expect the advice provided by the system to be
_reasonable_ , i.e. the speed should reflect the current state of the
environment and task, and not request the users to take an action that can be
seen as _extreme_ or out of line with social conventions. Multiple
participants describe the notion of a “sweet spot" (P05) that they were hoping
the advice would hit. The specification for the _sweet spot_ varied between
participants in terms of the advised speed range and the headway to their
leader. In our study, some participants preferred advice in the range of 25 to
30 mph, while others preferred advice in the slightly higher range of 30 to 35
mph. Similarly, some drivers preferred leaving the length of one car’s
distance between them and their leader, while others preferred much larger
distances of up to the length of three cars. Systems that suggested advice
corresponding to the driver’s _sweet spot_ are viewed favorably.

> “It actually kept me at… 25 mph for a longer period of time, which I liked.
> When the cars were like, you know, a [good] distance in front of me." - P04

In contrast, participants disliked when the advice did not fit their _sweet
spot_ requirement. As it pertains to congestion mitigation, speed advice that
was either too slow or too fast was not preferred and considered as “extreme"
(P04). While drivers felt that speeds that were too slow were “safe" (P13) and
provided “good control of the car" (P13), they also viewed them as counter-
intuitive and mentioned that slow speeds led to inefficiencies, i.e. were
“causing a pile up" (P07) rather than help avoid one. Participants also
reported feeling peer “pressure of seeing that long line of cars behind
[them]" (P10) when following such slow advice as it led to a violation of the
social norms that they were accustomed to. Particularly, users were weary of
being blamed by the cars behind them for holding up traffic when they were
simply trying to follow the advice from the system. In addition to being
stressed, frustrated, and “irritated" (P10) by these aspects of slow advice,
participants said that they would “not want to follow these instructions, even
if they [were] below the speed limit" (P07).

> “It was [asking me to go] too slow. I feel like I was the one who was making
> this congestion, so I was a little stressed out when there was no cars in
> front [of me]… It was just frustrating. " - P02

All 16 drivers reported that they felt very uncomfortable following advice
that was too fast as it would result in unsafe behavior, such as collisions or
off-roading. Participants unanimously chose to “just ignore the recommendation
and follow [their] own mind[s]" (P12), on being recommended advice that was
too fast. Such advice was “hard to follow" (P02) and made users “lose control"
(P12) of the vehicle and ultimately led to participants losing faith in the
abilities of the system. Thus, we recommend that CoRTA systems should
carefully craft advice to appear accurate as defined by drivers and their
personal _sweet spots_. We believe that this finding further motivates the
development of adaptive technology in CoRTA systems that enables
personalization to different drivers in line with the findings of Walch et al.
[31] for SAE Level 3 systems.

The Changes in Advice: Participants disliked policies that advised significant
speed jumps (i.e. large changes in the magnitude of the advice) and frequent
or sudden unexpected changes. The primary rationale for this sentiment was the
increase in mental load in order to react to large and/or rapid changes that
made driving less enjoyable. For example, P02 mentioned that the wavering of
the recommended range between “a high range and then suddenly going down to
[a] little range… It was a little stressful for me." Such behaviors also made
the advice hard to follow and left drivers unsatisfied, ultimately leading to
them “not following it" (P03).

Additionally, advice that oscillated between a set of values, often between
slow (e.g. 15 mph) and fast (e.g. 60 mph), was found to be undesirable and
“frustrating" (P04). P11 mentioned that such advice felt “pointless," as
trying to follow any of the speeds in the set was “causing an issue" with the
rest of the traffic, where they were forced between two undesirable behaviors
– they were either “catching up to the rest of the pack or [they were] the one
that was creating a pack". P04 also tried to rationalize this oscillatory
behavior as an artifact of the system aiming to keep the driver engaged while
encouraging them to maintain a certain speed. They said that they wished the
policy would be more _transparent_ and just advice the average of the set of
speeds:

> “It was constantly bouncing between 15/20 mph and 50/60 mph. And so, the
> effect was, realistically, my speed was somewhere between. Which I can see
> why it would want to keep me at that speed. But it was frustrating as a
> user… Well, if it wants me to go that speed, why doesn’t it just put the bar
> there? Because that’s where I’m driving, in effect, when it bounces back and
> forth like that." - P04

Interestingly, on the other end of the spectrum, advice that was constant,
i.e. did not change for a long period, was also not preferred as it led to
driver disengagement, even though it was easy and (usually) safer to follow.
Drivers referred to policies suggesting such constant advice over multiple
hold-length periods as “conservative" (P02) (We discuss the effects of the
hold-length itself in Section III-A). While constant advice was more
acceptable than rapidly changing advice, it made drivers question if the
policy was _actually_ cooperative as they felt like the system was “less
reactive" (P01). This constant behavior also led to interesting artifacts
where the system would not ask drivers to slow down when they were approaching
the car in front of them which users described as unsafe and dangerous.

> “I think it would be better if the speed changed in a more continuous
> fashion and that’s like feasible with the acceleration and deacceleration of
> the car. Instead of jumping from like 30 mph to 80 mph. If it slowly moved
> up that way, then it’s okay – I can follow that." - P04

Drivers expressed that they preferred advice that was “changing" because “in
this case it’s a congestion. So, I think it makes sense to change the speed"
(P12). Similar to the _sweet spot_ in advice value, drivers prefer policies
that provided smooth, gradual changes, where the rate of these changes is
dependent on the drivers’ style. Advice that changed gradually, i.e. in small
increments, and allowed users to react comfortably enhanced the driving
experience. P11 in particular mentioned that having “less big" jumps between
consecutive actions made it “more manageable to get to the" advised action.
They viewed such actions as “something I could actually do" as opposed to “an
unreachable goal" that they attributed to the advice provided by the systems
with large jumps. Participants specifically mentioned that they would be more
likely to use and follow a system that provided smooth advice to them.

Therefore, we further recommend that the cooperative systems should also
include the drivers’ preferred rate of change of advice in its _sweet spot_
determination. Furthermore, we also recommend that CoRTA systems should be
transparent and direct in their intent, i.e. the system should not communicate
auxiliary advice that is meant to lead drivers into causing the effects of the
actual intended advice. Such manipulative interactions are viewed highly
unfavorably by drivers.

The Hold-length: The time that the advised action was designed to be held
constant, i.e. the hold-length $\delta$ of the policies, had a significant
effect on the users’ experience. Generally, longer hold-lengths (10s in our
case) are preferred as they allow users with ample time to follow the advice.
Meanwhile, shorter hold-lengths (both 5s and 7s) either did not give enough
time for drivers to react or were stress inducing. Particularly, P13 mentioned
that the policies with shorter hold-lengths were asking them to “do too much
in too little time." Participants felt unsatisfied by policies that would
change the advice as soon as they had achieved it as it left them frustrated
and annoyed.

> “The fact that it kept changing as soon as I reached the desired speed. The
> first question I marked here was this one [pointing to the frustration
> question. Researcher: “Super frustrating?"] I think if it was a real-life
> scenario and it did that, I would just completely throw it out the window.
> [Talking to the policy] You don’t deserve to be telling me what to do. You
> don’t know what you’re doing." - P10

We hypothesize that there exists an ideal dynamic time-period that would
satisfy drivers. Such an ideal time-period would depend on the drivers’
personal preferences, attributes of the advice (e.g. the magnitude of change),
and environmental factors. A dynamic hold-length that depends on the above
factors would ideally address the issues discussed above and lead to better,
more reactive and cooperative systems. We present quantitative results in the
form of ANOVA tests on metrics collected from the questionnaires that
corroborate these qualitative findings in Appendix A.

The Understandability of the Advice: Aside from wanting accurate advice,
drivers are also particular about understanding the advice provided to them.
Systems that relayed advice that matched what drivers would do normally were
touted as more “realistic" (P14) and hence more usable. P09 called their
experience with a policy “fun" because “I like that it almost matched what we
were doing… it almost made sense what kind of behavior it was trying to
achieve."

> “I did like it, because I think in that one it made sense was trying to be
> achieved and the driving speed was one that was more natural." - P09

In contrast, advice that differed from the drivers’ world view was seen as
“annoying" (P04) and “frustrating" (P10), and hence influenced drivers to
ignore or “disobey" (P15) the system. For example, P02 called a system
“garbage" as its suggestions were “the opposite of what I would do."
Additionally, advice that was perceived as confusing negatively impacted
participants’ trust in the systems’ abilities.

> “I was also confused why it was changing the speed on me when the cars in
> front of me were pretty far ahead for most of the simulation, so I didn’t
> really understand why all of a sudden [it would] decide I should be going 5
> miles[sic] faster, oh, now I need to go 5 miles[sic] slower. So, because of
> that, I was kind of like, okay, I don’t know how much I should really follow
> this advice… I just, I couldn’t understand why it was telling me what it
> was" - P04

In the case of traffic advisory systems, users prefer instructions that
reflect the current state of traffic and allow them to blend into the flow of
traffic, to be viewed as reasonable by others on the road. For example, P05
mentioned that they “didn’t feel like [they were] fitting into like the rest
of the traffic" when the policy was advising a speed of “20 mph and there
[were] no cars in front of [them]." All drivers mentioned that they would be
less likely to use systems that do not satisfy this requirement in real life
and would rather “throw [the system] out the window" (P10) and even reconsider
purchasing vehicles with such systems.

> “If I have to use a system then I will have to know why it’s asked me to go
> that fast. Which is really unreasonable for me… So, I would definitely ditch
> this system. I would not buy the car." - P16

We highly recommend that the advice given to drivers should also be easily
understandable, in addition to the aforementioned personalizations. We foresee
the inclusion of these personalizations as a major challenge for the
development CoRTA systems as they would be required to address the various
user needs _and still_ also fulfill the downstream tasks that they are
designed to address. Furthermore, participants noted their understanding of
the system as a determinant of their trust in the system.

> “It felt like it had no understanding of the distance between me and the car
> in front. So, because of that, I didn’t really trust the system that much."
> - P04

Trusting the Advice: Users mentioned that the presence of inconsistencies
during a trial decreased their trust in the system, even if they had
experienced desirable behaviors during the trial. For example, P03 very
bluntly stated during the post-study interview that “I would not fully trust
any of them", without providing a reason, even though they had previously
expressed positive reactions to some systems. We conjecture that this mistrust
is due to a mismatch between their driving style and the policies’
recommendations that was brought up during multiple trials: “I’m in conflict
with that advised speed a lot", “mismatch is because of [my] driving style",
“I’m contradictory with the system."

Furthermore, the contrast between the drivers’ past driving experiences and
the advice given by the system was a significant factor that influenced the
drivers’ trust in the system. To this point, P10 recalled that the system “was
just going after every basic instinct and foundation of driving my father
taught me." They specifically stated that this disagreement made them not want
to listen to the advice: “I don’t like listening to this." P10 also added that
as a consequence of this loss in trust they “would listen to [the advice for]
2 minutes and then immediately ignore it" if they were driving “in the real
world" rather than in the simulator.

8 of 16 participants mentioned that they would ignore the advice in some form
due to their loss in trust in the system. Drivers wanted to partially ignore
the advice if the system was somewhat reasonable and only faltered sometimes
(e.g. provided incredibly high speed advice once while being stable
otherwise). In particular, P16 noted that if the system “gave me some like
unreasonable commands that’s pretty high, but also in some [cases] lower ones
which are kind of reasonable," that they would “just completely ignore the
higher ones" and use those recommendations “just like an indicator" to “speed
up a little bit". In more extreme circumstances, i.e. if the system exhibited
unstable behavior or inconsistencies, drivers reported wanting to completely
ignore the system and were only following the advice solely because they were
asked to for the study. This is illustrated in the following interaction with
P10:

> Researcher: What did you think of this trial?
> P10: It was great once I opted to ignore all the inputs [laughs]
> Researcher: What did you think about what it was asking you to do?
> P10: Unreasonable. I think it was asking me to slow down when there was
> clearly a lot of room in front of me. Sometimes it was asking me to speed up
> when I could see [that] I would have to slam the brakes if I tried to do
> that. And so, I was like, well, I have a better decision making than this
> system.

Therefore, we also recommend that CoRTA systems should adapt to the long-term
driver behaviors and their past histories so that participants deem the system
to be _reasonable_. We posit that such adaption would lead to an increase in
trust and therefore usage which would directly impact the downstream task that
the system is designed to address.

###  III-B “What did you think of the User Interface?"

Participants found the use of the speedometer to communicate the advice
controversial. P06 said that the UI was “definitely pretty easy to follow… but
at times it was a bit distracting." This sentiment was shared by 8 other
participants, where P09, in particular, felt strongly that “staring at the
speedometer is not safe." Users shared that driving was already a load
incentive task due to the requirement of paying attention to the road and the
other vehicles and adding “any kind of instructions is actually another layer
of burden that you impose on the driver" (P12). Thus, the increased load
diverted drivers away from the main driving task “overwhelmed" (P06) them and
created opportunities for unsafe scenarios. However, 3 of 9 participants noted
that the use of the speedometer was a major issue only in the cases where the
behavior of the advice increased the drivers’ mental load. Therefore, we
advise selective use of this visual modality.

> “If it’s not constantly changing, then I don’t think it’s an issue. But if
> it’s constantly changing then I have to judge if the change is a valid one
> or not. And I have to constantly think about that, which is not good." - P12

Some drivers mentioned that they viewed the advice solely as a suggestion and
only kept the speedometer in their peripheral vision, as they would in real
life. These drivers mentioned that doing so “eased the amount of mental load a
little bit" (P05) but adversely affected how precise they were in following
the advice. Furthermore, the use of the green bar to indicate an acceptable
range was touted by drivers as “helpful" (P12) as it gave them more
“flexibility" (P14). P11 mentioned that the green bar facilitated easier
instruction adherence as finding “the right amount to press the pedal, for
example, to get exactly on the red line is pretty difficult." However, some
drivers felt that “the margin was a little small" (P16) and wished that the
green range had been a little larger to allow for more leeway in how precisely
the advice was followed, “but it wasn’t a big deal" (P16).

Participants also reacted positively to coloring the speed value (displayed on
the speedometer) green when they were driving within the advised range and
proclaimed it as a “good touch" (P15). P15 also mentioned that changing the
color of the number “made me look at the actual [gauge] less and just look at
the big number and that didn’t take as much attention". The color choice also
helped in minimizing the load on participants as they were “looking for green"
(P05) in their peripheral vision. Therefore, we conclude that using attractive
colors decreases the mental load on participants and enables easier
instruction following. Additionally, allowing a comfortable acceptable error
bar (green range) would improve user experiences, but we find that this range
differed between drivers. We encourage further work to estimate the width of
the acceptable error range based on the driver preferences.

The use of the color green and the dynamics of changing colors if the advice
was followed, “gamified (sic)" (P10) the system. The green color scheme
improved interactions with the system and also kept drivers engaged.
Participants were actively aiming to visualize green on their speedometer
simply because “seeing green is fun" (P10). In this particular scenario, P10
was disappointed when the policy would change the advised action not soon
after they achieved it because they did not “get the satisfaction of getting
the green number. And so that was frustrating." They went on to say that they
would have been less frustrated “if it gave me one little second of being
green" (P10). However, we note the induction of such “gamified" reactions
might be unsafe. We observed that it was easy for participants to get
distracted and lose sight of “what’s in front of [them] as much because
[they’re] focused on putting the white[sic] inside the green part" (P09).

When asked about alternate suggestions for interface modes, one participant
recommended that “voice command[s] or the indicator light would be better"
(P01) to interface with drivers. While the 15 others preferred visual
interfaces. Heads-Up Displays (HUDs) were a popular alternative suggested by 3
participants (others had no opinions), as it would prevent them from looking
down at the speedometer and hence allow them to focus on the road. Some
participants voiced a preference for being advised a change in action (e.g. go
slower, maintain speed, go faster) over being advised an exact action.
Communicating this information through the use of directional arrows on the
HUD was suggested. We refrain from providing a staunch recommendation on the
interaction mode as we did not quantitatively study the UI.

###  III-C “Did You Find Yourself Adapting to the System?"

While users have preferences for the systems’ behavior, we observed that
drivers adapted their own driving during their interactions with the system in
an effort to improve their experience. Towards the latter trials, some users
formed simple rules around when and how they would follow advice. These rules
differed for each participant and were informed by their previous experiences
with the different policies and their behaviors. For example, P16 said that
they would always check the distance to the car ahead of them first. Then if
they determined the distance was large enough and their speed was less than or
at 30 mph they would “feel comfortable driving a little faster," if the policy
suggested it. Else, they would only listen to the policy if it advised them to
slow down. Otherwise, they would “do nothing." Alternately, we observed that
P07 would ignore all advice that was over 20 mph as they felt “unable to
control the vehicle at larger speeds", regardless of their distance to the car
ahead of them. While P07 did not provide a reason for their decision, we posit
that they formed their rule due to their limited driving experience (1 year or
less), and after almost crashing during an early trial. Similarly, P15
mentioned that they also formulated similar rules and “games" in real life:

> “I do this when I’m driving a regular car as well, where I try to keep the
> speed at a certain number and just hold it there consistently. I think of it
> kind of like a game where you’re holding the pedal down just enough where
> it’s not going up or down." - P15

Additionally, participants also exhibited behaviors based on rationalizations
that they arrived at by anticipating certain behaviors of the policy. When
asked why P09 had driven faster than the advice given during a trial, they
mentioned that “it was going slow, and I didn’t want it to go slow because the
distance between the next car and us was quite large… We had just slowed down,
so I knew we weren’t going to slow down again any time soon." Particularly,
participants _expected_ the policies to behave similarly, even though the
policies clearly exhibited different behaviors. Note that, information about
the policies (if they were similar or different) was intentionally withheld
from participants to observe if such expectations would arise. For example, in
one instance P04 purposefully disregarded an advised action as they were
anticipating the policy to change its advice at a later time, due to their
experience in previous trials.

> “I’ve been gaming the system a little bit because I’ve been expecting it to,
> you know, it’ll recommend something high and then recommend something low.
> And this time it felt like it would recommend two things that were high. And
> then it’d be like, I guess I’m going to accelerate now. And then it would
> drop low, but it wouldn’t drop it like far below what I had accelerated to.
> It felt like this is a little bit easier to match the speed. It’s like I
> accelerated and now I just have to go back down a little bit." - P04

Participants viewed the need to adapt to the system as normal and said that it
was not cumbersome or annoying. For example, P08 mentioned that using the
system gave them “the same feeling [they] get when [they] drive a car [they]
haven’t driven before". P13 elaborated that using the system was akin to the
“standard experience" with “any car you drive for the first time" where “you
kind of have to get used to the features."

###  III-D “Would you use these systems?"

From the results of the questionnaires and interactions with participants we
conclude that the policies tested during the study are not deployable without
modifications, due to the drawbacks discussed previously. However, while the
systems might not suggest perfectly accurate advice, participants still found
auxiliary uses for the policies. First, participants mentioned using the
system to affirm their own driving styles and speeds. Particularly, P12
mentioned the advised speed “reassure[d] that the speed that [they] want to
drive is correct" when the advice “matches [their] optimal speed in [their]
mind". Others mentioned their interactions with the system made them feel
“very validated in [their] driving choices" (P10). Second, users said that
they would suggest the usage of this system as a teaching tool for new
drivers. For example, P13 revealed: “my younger sister is learning how to
drive right now. If you told me I could put [the system] in her car, I would".
In P13’s experience, they believed the _conservative_ systems that they
experienced in some trials would aide in teaching their sibling how to drive
safely. Lastly, the use of the system in new environments was mentioned as a
potential use case: “If [I] was maybe driving in an unfamiliar environment,
I’d try to follow this a bit more" (P14).

Participants proclaimed that they would want to adopt CoRTA systems if there
was a clear benefit for their usage. P08 said that they would employ the
system if they were “convinced by whatever benefits" of the system. In
particular, P08 mentioned environmental benefits of congestion mitigation
agents, if proven to work, would motivate them. Others agreed that they would
use such a system were it be proven to have altruistic benefits (e.g.
environmental benefits).

> “So, I feel like it’s kind of asking the user to [make a] contribution to
> society… It could result in a good way, in a sense that everyone contributes
> to that and make the whole[sic] traffic better. So, those type of
> [motivations], I buy that myself. So, I think it’s good. I feel like, people
> would buy that. But there are definitely some users who just don’t care." -
> P16

In general participants had a positive outlook towards ideal CoRTA systems.
P06 mentioned that this driving technology was “something that should
definitely be looked into for the future." They acknowledged that, while self-
driving solutions might address such goals, they thought that these systems
are “definitely something that should be looked into" for “having advice for
non-self-driving vehicles." Therefore, we posit that future CoRTA systems
built for altruistic purposes would be widely accepted by young drivers, _if_
they are personalized as postulated in our discussion.

##  IV Conclusion and Future Work

Limitations and Future Work: While the findings presented above provide
important insights into driver interactions with advisory systems, our methods
are not without limitations. Firstly, we believe that a larger user study with
a more diverse population is merited due to the smaller range in participant
experiences in our study. Particularly, while our population addresses the
sentiments of a significant portion of drivers, it does not reflect the needs
of the majority. Secondly, our participants experienced all study procedures
in a lab environment with a driving simulator. While driving simulators are
helpful in evaluating user sentiments in automotive settings, they lack the
psychological and physical level feedback mechanisms offered by naturalistic
driving. A naturalistic driving study in a controlled environment to evaluate
CoRTA systems would provide further credence to our findings. Lastly, our
study was designed to capture qualitative user sentiments for one style of UI
design. Performing a comparative study with multiple other UI options would be
conducive in arriving at a definite UI framework for CoRTA systems. Therefore,
a systematic study that includes such definite quantitative methods to capture
user perceptions would corroborate our findings.

Conclusions: In this article, we present our findings from a driving simulator
user study (N=16) conducted with young adults. We present insights into how
drivers interact with cooperative real-time advisory systems and discuss
various preferences and reactions for such interactions. Our findings suggest
that users prefer smooth-flowing, easy to understand advice that is also
personalized to reflect their driving styles. We also find that the use of
attractive colors in the UI of these advisory systems improves user
experiences and driver engagement. Additionally, agreement in systems’
behavior and users’ past experiences contributes significantly to drivers’
trust in the system. Lastly, find that drivers anticipate advice from the
systems and adapt their driving mannerisms accordingly. We anticipate the use
of these _lessons in cooperation_ in the design and development of future
automotive assistive systems.

## Acknowledgments

The authors would like to thank all user study participants. We would also
like to thank Dr. Kristina Miller, Jung-Hoon Cho, Sirui Li, and Dr. Jeongyun
Kim for their support and assistance with various aspects of this work.

## References

  * Bark et al. [2014] K. Bark, C. Tran, K. Fujimura, and V. Ng-Thow-Hing, “Personal navi: Benefits of an augmented reality navigational aid using a see-thru 3d volumetric hud,” in _Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 1–8.
  * Bolton et al. [2015] A. Bolton, G. Burnett, and D. R. Large, “An investigation of augmented reality presentations of landmark-based navigation using a head-up display,” in _Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’15. New York, NY, USA: Association for Computing Machinery, 2015, p. 56–63.
  * Nguyen et al. [2016] V. Nguyen, O. T. T. Kim, T. N. Dang, S. I. Moon, and C. S. Hong, “An efficient and reliable green light optimal speed advisory system for autonomous cars,” in _2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS)_ , 2016, pp. 1–4.
  * Obuhuma et al. [2018] J. Obuhuma, H. Okoyo, and S. Mcoyowo, “Real-time driver advisory model: Intelligent transportation systems,” in _2018 IST-Africa Week Conference (IST-Africa)_ , 2018, pp. Page 1 of 11–Page 11 of 11.
  * Chada et al. [2023] S. K. Chada, D. Görges, A. Ebert, R. Teutsch, and S. P. Subramanya, “Evaluation of the driving performance and user acceptance of a predictive eco-driving assistance system for electric vehicles,” _Transportation Research Part C: Emerging Technologies_ , vol. 153, p. 104193, 2023.
  * Chada et al. [2022] S. K. Chada, D. Görges, A. Ebert, R. Teutsch, and C. G. Min, “Learning-based driver behavior modeling and delay compensation to improve the efficiency of an eco-driving assistance system,” in _2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)_ , 2022, pp. 415–422.
  * Chen and Lazar [2022] Y. Chen and M. Lazar, “Driving mode advice for eco-driving assistance system with driver reaction delay compensation,” _IEEE Transactions on Circuits and Systems II: Express Briefs_ , vol. 69, no. 1, pp. 134–138, 2022.
  * Birrell et al. [2014] S. A. Birrell, M. Fowkes, and P. A. Jennings, “Effect of using an in-vehicle smart driving aid on real-world driver performance,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 15, no. 4, pp. 1801–1810, 2014.
  * Lin et al. [2014] X. Lin, D. Görges, and S. Liu, “Eco-driving assistance system for electric vehicles based on speed profile optimization,” in _2014 IEEE Conference on Control Applications (CCA)_ , 2014, pp. 629–634.
  * Di Lena et al. [2017] P. Di Lena, S. Mirri, C. Prandi, P. Salomoni, and G. Delnevo, “In-vehicle human machine interface: An approach to enhance eco-driving behaviors,” in _Proceedings of the 2017 ACM Workshop on Interacting with Smart Objects_ , ser. SmartObject ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 7–12.
  * Kraus et al. [2016] J. M. Kraus, F. Nothdurft, P. Hock, D. Scholz, W. Minker, and M. Baumann, “Human after all: Effects of mere presence and social interaction of a humanoid robot as a co-driver in automated driving,” in _Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’16 Adjunct. New York, NY, USA: Association for Computing Machinery, 2016, p. 129–134.
  * Hock et al. [2016] P. Hock, J. Kraus, M. Walch, N. Lang, and M. Baumann, “Elaborating feedback strategies for maintaining automation in highly automated driving,” in _Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. Automotive’UI 16. New York, NY, USA: Association for Computing Machinery, 2016, p. 105–112.
  * Large et al. [2019] D. R. Large, G. Burnett, D. Salanitri, A. Lawson, and E. Box, “A longitudinal simulator study to explore drivers’ behaviour in level 3 automated vehicles,” in _Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 222–232.
  * Pakdamanian et al. [2022] E. Pakdamanian, E. Hu, S. Sheng, S. Kraus, S. Heo, and L. Feng, “Enjoy the ride consciously with cawa: Context-aware advisory warnings for automated driving,” in _Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 75–85.
  * Tran et al. [2013] C. Tran, K. Bark, and V. Ng-Thow-Hing, “A left-turn driving aid using projected oncoming vehicle paths with augmented reality,” ser. AutomotiveUI ’13. New York, NY, USA: Association for Computing Machinery, 2013, p. 300–307.
  * Yan et al. [2016] F. Yan, M. Eilers, M. Baumann, and A. Luedtke, “Development of a lane change assistance system adapting to driver’s uncertainty during decision-making,” in _Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’16 Adjunct. New York, NY, USA: Association for Computing Machinery, 2016, p. 93–98.
  * Trabelsi et al. [2023] Y. Trabelsi, O. Shabat, J. Lanir, O. Maksimov, and S. Kraus, “Advice provision in teleoperation of autonomous vehicles,” in _Proceedings of the 28th International Conference on Intelligent User Interfaces_ , ser. IUI ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 750–761. [Online]. Available: https://doi.org/10.1145/3581641.3584068
  * Kim et al. [2016] H. Kim, A. Miranda Anon, T. Misu, N. Li, A. Tawari, and K. Fujimura, “Look at me: Augmented reality pedestrian warning system using an in-vehicle volumetric head up display,” in _Proceedings of the 21st International Conference on Intelligent User Interfaces_ , ser. IUI ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 294–298.
  * of Automotive Engineers [2021] S. of Automotive Engineers, “SAE Levels of Driving Automation™ Refined for Clarity and International Audience,” 2021. [Online]. Available: https://www.sae.org/blog/sae-j3016-update
  * Lee and Cheng [2008] W.-C. Lee and B.-W. Cheng, “Effects of using a portable navigation system and paper map in real driving,” _Accident Analysis & Prevention_, vol. 40, no. 1, pp. 303–308, 2008.
  * Mamun et al. [2017] M. A. A. Mamun, J. A. Puspo, and A. K. Das, “An intelligent smartphone based approach using iot for ensuring safe driving,” in _2017 International Conference on Electrical Engineering and Computer Science (ICECOS)_ , 2017, pp. 217–223.
  * Kalra and Paddock [2016] N. Kalra and S. M. Paddock, _Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability?_ RAND Corporation, 2016.
  * Dixit et al. [2016] V. V. Dixit, S. Chand, and D. J. Nair, “Autonomous vehicles: Disengagements, accidents and reaction times,” _PLOS ONE_ , vol. 11, no. 12, pp. 1–14, 12 2016.
  * Mazein [2024] E. Mazein, “Autonomous driving is “happening”, but slower than expected,” Jan 2024.
  * Stacy and Meixell [2018] C. P. Stacy and B. Meixell, “Self-driving cars could harm low-income people if we don’t prepare for their rise,” Sep 2018. [Online]. Available: https://www.urban.org/urban-wire/self-driving-cars-could-harm-low-income-people-if-we-dont-prepare-their-rise
  * Metz [2021] C. Metz, “The costly pursuit of self-driving cars continues on. and on. and on.” May 2021. [Online]. Available: https://www.nytimes.com/2021/05/24/technology/self-driving-cars-wait.html
  * Zali et al. [2022] N. Zali, S. Amiri, T. Yigitcanlar, and A. Soltani, “Autonomous vehicle adoption in developing countries: Futurist insights,” _Energies_ , vol. 15, no. 22, 2022.
  * Shahab et al. [2010] Q. Shahab, J. Terken, and B. Eggen, “Auditory messages for speed advice in advanced driver assistance systems,” in _Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’10. New York, NY, USA: Association for Computing Machinery, 2010, p. 50–56.
  * Jayawardana and Wu [2022] V. Jayawardana and C. Wu, “Reinforcement learning for eco-lagrangian control at intersections,” in _European Control Conference_ , 2022.
  * Hasan et al. [2023] A. Hasan, N. Chakraborty, H. Chen, J.-H. Cho, C. Wu, and K. Driggs-Campbell, “PeRP: Personalized residual policies for congestion mitigation through co-operative advisory systems,” in _IEEE International Conference on Intelligent Transportation Systems (ITSC)_ , 2023.
  * Walch et al. [2017] M. Walch, K. Mühl, J. Kraus, T. Stoll, M. Baumann, and M. Weber, _From Car-Driver-Handovers to Cooperative Interfaces: Visions for Driver–Vehicle Interaction in Automated Driving_. Cham: Springer International Publishing, 2017, pp. 273–294.
  * Pichen et al. [2021] J. Pichen, T. Stoll, and M. Baumann, “From sae-levels to cooperative task distribution:an efficient and usable way to deal with system limitations?” in _13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 109–115.
  * Woide et al. [2023] M. Woide, L. Miller, M. Colley, N. Damm, and M. Baumann, “I’ve got the power: Exploring the impact of cooperative systems on driver-initiated takeovers and trust in automated vehicles,” in _15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’23)_. Ingolstadt, Germany: ACM, 2023, pp. 1–13.
  * Wintersberger et al. [2020] P. Wintersberger, H. Nicklas, T. Martlbauer, S. Hammer, and A. Riener, “Explainable automation: Personalized and adaptive uis to foster trust and understanding of driving automation systems,” in _12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 252–261.
  * Kraut et al. [2023] V. Kraut, L. Motnikar, A. G. Mirnig, and P. Fröhlich, “Assertive takeover requests: Immediate and sustained effects on stress and performance,” _Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , 2023.
  * Helldin et al. [2013] T. Helldin, G. Falkman, M. Riveiro, and S. Davidsson, “Presenting system uncertainty in automotive uis for supporting trust calibration in autonomous driving,” in _Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’13. New York, NY, USA: Association for Computing Machinery, 2013, p. 210–217.
  * Peng et al. [2023] C. Peng, İbrahim Öztürk, S. Nordhoff, R. Madigan, S. Hoogendoorn-Lanser, M. Hagenzieker, and N. Merat, “Exploring user comfort in automated driving: A qualitative study with younger and older users using the wizard-of-oz method,” in _Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. ACM International Conference Proceeding Series. Ingolstadt, Germany: Association for Computing Machinery (ACM), 2023, pp. 342–345.
  * Vongvit et al. [2022] R. Vongvit, J. Lee, M. Itoh, and S. C. Lee, “Meta-trend of trust factors in autonomous vehicles,” in _Adjunct Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 94–98.
  * DeGuzman and Donmez [2021] C. A. DeGuzman and B. Donmez, “Knowledge of and trust in advanced driver assistance systems,” _Accident Analysis & Prevention_, vol. 156, p. 106121, 2021.
  * Wilfinger et al. [2010] D. Wilfinger, A. Meschtscherjakov, M. Murer, and M. Tscheligi, “Influences on user acceptance: Informing the design of eco-friendly in-car interfaces,” in _Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications_ , ser. AutomotiveUI ’10. ACM, 2010, pp. 125–128.
  * Asadi and Vahidi [2011] B. Asadi and A. Vahidi, “Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time,” _IEEE Transactions on Control Systems Technology_ , vol. 19, no. 3, pp. 707–714, 2011.
  * Fleming et al. [2021] J. Fleming, X. Yan, and R. Lot, “Incorporating driver preferences into eco-driving assistance systems using optimal control,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 22, no. 5, pp. 2913–2922, 2021.
  * Kreidieh et al. [2018] A. R. Kreidieh, C. Wu, and A. M. Bayen, “Dissipating stop-and-go waves in closed and open networks via deep reinforcement learning,” in _IEEE International Conference on Intelligent Transportation Systems_ , Nov 2018, pp. 1475–1480.
  * Vinitsky et al. [2018] E. Vinitsky, K. Parvate, A. Kreidieh, C. Wu, and A. Bayen, “Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion,” in _IEEE International Conference on Intelligent Transportation Systems_ , Nov 2018, pp. 759–765.
  * Yan and Wu [2021] Z. Yan and C. Wu, “Reinforcement Learning for Mixed Autonomy Intersections,” in _IEEE International Intelligent Transportation Systems Conference_ , Sep 2021, pp. 2089–2094.
  * Yan et al. [2022] Z. Yan, A. R. Kreidieh, E. Vinitsky, A. M. Bayen, and C. Wu, “Unified Automatic Control of Vehicular Systems With Reinforcement Learning,” _IEEE Transactions on Automation Science and Engineering_ , pp. 1–16, 2022.
  * Han et al. [2024] J. Han, T. Ard, R. Wang, P. Gupta, A. Vahidi, Y. Jia, and D. Karbowski, “Human Driver Interaction with Eco-Speed Advisory System in Connected Vehicles: Simulation and Experimental Results,” in _103rd Transportation Research Board (TRB) Annual Meeting_ , Jan. 2024.
  * Wu et al. [2021] C. Wu, A. R. Kreidieh, K. Parvate, E. Vinitsky, and A. M. Bayen, “Flow: A modular learning framework for mixed autonomy traffic,” _IEEE Transactions on Robotics_ , 2021.
  * Sridhar and Wu [2021] M. Sridhar and C. Wu, “Piecewise constant policies for human-compatible congestion mitigation,” in _IEEE International Intelligent Transportation Systems Conference_ , 2021.
  * Cho et al. [2023] J.-H. Cho, S. Li, J. Kim, and C. Wu, “Temporal transfer learning for traffic optimization with coarse-grained advisory autonomy,” 2023.
  * Stern et al. [2018] R. E. Stern, S. Cui, M. L. Delle Monache, R. Bhadani, M. Bunting, M. Churchill, N. Hamilton, R. Haulcy, H. Pohlmann, F. Wu, B. Piccoli, B. Seibold, J. Sprinkle, and D. B. Work, “Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,” _Transportation Research Part C: Emerging Technologies_ , vol. 89, pp. 205–221, 2018.
  * Dosovitskiy et al. [2017] A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in _Proceedings of the 1st Annual Conference on Robot Learning_ , 2017, pp. 1–16.
  * Treiber et al. [2000] M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” _Physical review E_ , vol. 62, no. 2, p. 1805, 2000.
  * Hart [1986] S. G. Hart, “Nasa task load index (tlx),” 1986.
  * Brooke [1991] J. Brooke, “SUS — A quick and dirty usability scale,” 1991.
  * Li et al. [2023] S. Li, R. Dong, and C. Wu, “Stabilization Guarantees of Human-Compatible Control via Lyapunov Analysis,” in _2023 European Control Conference (ECC)_ , Jun. 2023, pp. 1–8.
  * Kingma and Welling [2014] D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in _International Conference on Learning Representations (ICLR)_ , 2014.
  * Lopez et al. [2018] P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in _The 21st IEEE International Conference on Intelligent Transportation Systems_ , 2018.
  * CARLA [2024] CARLA, “Sumo co-simulation,” 2024. [Online]. Available: https://carla.readthedocs.io/en/latest/adv_sumo/
  * Hasan et al. [2022] A. Hasan, N. Chakraborty, C. Wu, and K. Driggs-Campbell, “Towards co-operative congestion mitigation,” in _Proceedings of the ‘Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust Workshop‘ at the IEEE International Conference on Robotics and Automation_ , 2022.

##  Appendix A Quantitative Results based on the hold-length

The quantitative results in Table II are in agreement with the qualitative
feedback discussed in Section III-A. The table shows aggregates for the Raw
TLX Score computed from the NASA TLX questionnaire, the SUS Score, and the
usage score computed with simple Likert scale statistical analysis (1=Very
Unlikely; 5=Very Likely) on the responses to the question “How likely are you
to use this system while driving on the highway?".

TABLE II: Quantitative Metrics from the user study Hold-length ($\delta$) | Raw TLX $(\downarrow)$ | SUS $(\uparrow)$ | Usage Score $(\uparrow)$
---|---|---|---
50 | 10.39 $\pm$ 3.76 | 46.97 $\pm$ 22.41 | 1.85 $\pm$ 1.07
70 | 8.81 $\pm$ 3.85 | 59.43 $\pm$ 19.86 | 2.23 $\pm$ 1.09
100 | 6.49 $\pm$ 2.57 | 69.89 $\pm$ 19.45 | 2.81 $\pm$ 1.42

We observe a clear trend where systems with larger hold-lengths are determined
to be less load inducing (lower TLX) and more usable (higher SUS) in Table II.
Our findings are further corroborated by Univariate analysis of ANOVA tests
that indicate statistically significant differences in the effect of the hold-
length as the independent variable with the RAW TLX Score
($F_{2,144}=51.143,p<0.01$) and the SUS Score ($F_{2,144}=40.456,p<0.005$) as
dependent variables, respectively. While the TLX and SUS scores indicate that
participants would prefer to use the systems with larger hold-lengths, the
usage scores presented correspond to a general “Neutral" feeling. Therefore,
we conclude that the systems in their current state would not be desirable for
use in the real world without enhancements to address driver
concerns11footnotemark: 1. However, this low score is only a reflection of the
current accuracy of the tested congestion mitigation systems, and not of the
concept of CoRTA systems as a whole. We envision a better reception for future
real-time advisory systems that are developed to address the recommendations
presented in this work.

We note here that neither the policy type nor the order in which policies were
tested were statistically significant determinants for any qualitative metric.
Particularly, for the policy type as the independent variable we observed
$F_{2,144}=4.179,p>0.1$ and $F_{2,144}=0.504,p>0.5$ with the RAW TLX and SUS
Scores as dependent variables, respectively. Additionally, we also observed no
significant difference with the order in which the different policies were
introduced to participants as the independent variable. The ANOVA tests
produced F statistics of $F_{8,144}=0.280,p>0.9$ and $F_{8,144}=0.228,p>0.9$
for the Raw TLX and SUS scores as dependent variables, respectively.

##  Appendix B Piecewise Constant Policies

Formally, PC policies, $\pi_{PC}:S\rightarrow A$, process an observed state
$s_{t}\in S$ and output an action $a^{advice}=a_{t}\in A$, such that
$a^{advice}=a_{t}=a_{t+1}=\dots=a_{t+\delta-1}$. The driver is recommended the
action $a^{advice}$ and acts upon it in the vehicle according to their wishes.
We refer interested readers to the works by Sridhar and Wu [49], Li et al.
[56], Cho et al. [50], Jayawardana and Wu [29], and Hasan et al. [30] for a
more in depth discussion on PC policies and their variants11footnotemark: 1.

In this work, we study cooperative real-time advisory systems through PC
policies and two of its residual variants. The three policies are:

  1. 1.

PC Policy (PCP) [49]: The PC policy introduced above.

  2. 2.

PC Residual Policy (RP)11footnotemark: 1: A modified PC policy that appends a
residual action to the PCP action. Residual Policies trained with improvements
in the reward function and the driver modeling to account for driver reactions
to advice.

  3. 3.

PC Personalized Residual Policy (PeRP)11footnotemark: 1: A modified RP that
includes the use of a Variational autoencoder [57] that performs unsupervised
driver trait inference on how drivers react to advice.

TABLE III: A Details summary of our recommendations for the design of future
CoRTA systems

| Rec. | Behavior/attribute | Why
---|---|---|---
Advice | ✓ |  _sweet spot_ † speeds | Easy to follow
✗ |  Extreme (too slow† or too fast†) speeds | Leads to inefficiencies, Drivers feels peer pressure
✓ | Smooth, gradual† changes in advice | Easy to follow
✗ |  Large† change in magnitude | Hard to enact
✗ | Frequent or sudden change in advice | Hard to follow, Stressful, Leads to violation of social norms
✗ | No change in advice | Perceived as non-reactive or absent
✗ |  Oscillations between values in a set |  Manipulative, Stressful, Decreases trust in system
✓ |  Transparent and direct |  Increases trust in system, Does not confuse users
✓ | Matches user’s driving style† |  Perceived as reasonable, Increases trust in the system
✓ | Reflects current traffic state |  Increases trust by allowing driver to blend into traffic
✓ | Easy to understand | Increases trust, easy to follow
| ✓ |  Adapts to driver’s past history† |  Perceived as reactive, increases trust in system
User Interface | $\sim$ | Speedometer |  Easy to understand, Distracting and hence dangerous
✓ |  Visible in peripheral vision |  Easy to use
? |  Auditory Modes, icons |  -
? |  Heads-up Display |  Easy to read
✓ |  Use of colors† to indicate changes |  Easy to understand, Decreases mental load
✓ |  Comfortable† Acceptable Error bar |  Easy to use
✓ Recommended ✗ Not Recommended $\sim$Controversial †Personalized

All policies were trained in simulation and evaluated in both simulation and
through a driving simulator user study and are shown to be provably effective
in mitigating congestion with no sim-to-real transfer11footnotemark: 1.
Particularly, the policies are built on the FLOW framework [48] that allows
for simulation with the SUMO traffic simulator [58] and co-simulation [59, 60]
with the CARLA Simulator [52].