Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior

Abstract

Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human-subject study with 30 participants in a virtual reality environment. The study manipulated two factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk type (signalized and unsignalized crossing). Results indicate that pedestrians' trust in AVs was influenced by AV driving behavior as well as the presence of a signal light. In addition, the impact of the AV's driving behavior on trust in the AV depended on the presence of a signal light. There were also strong correlations between trust in AVs and certain observable trusting behaviors such as pedestrian gaze at certain areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also present implications for design and future research.

Full text

ORIGINAL RESEARCH
published: 28 November 2019
doi: 10.3389/frobt.2019.00117
Frontiers in Robotics and AI | www.frontiersin.org 1November 2019 | Volume 6 | Article 117
Edited by:
Daisuke Sakamoto,
Hokkaido University, Japan
Reviewed by:
Samuel Francisco Mascarenhas,
University of Lisbon, Portugal
George Yannis,
National Technical University of
Athens, Greece
Bhadradri Raghuram Kadali,
Visvesvaraya National Institute of
Technology, India
*Correspondence:
Lionel P. Robert Jr.
lprobert@umich.edu
Specialty section:
This article was submitted to
Human-Robot Interaction,
a section of the journal
Frontiers in Robotics and AI
Received: 14 March 2019
Accepted: 25 October 2019
Published: 28 November 2019
Citation:
Jayaraman SK, Creech C, Tilbury DM,
Yang XJ, Pradhan AK, Tsui KM and
Robert LP Jr (2019) Pedestrian Trust
in Automated Vehicles: Role of Traffic
Signal and AV Driving Behavior.
Front. Robot. AI 6:117.
doi: 10.3389/frobt.2019.00117
Pedestrian Trust in Automated
Vehicles: Role of Traffic Signal and
AV Driving Behavior
Suresh Kumaar Jayaraman 1, Chandler Creech 2, Dawn M. Tilbury 1, X. Jessie Yang 3,
Anuj K. Pradhan 4, Katherine M. Tsui 5and Lionel P. Robert Jr. 6
*
1Department of Mechanical Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, United States,
2Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI,
United States, 3Department of Industrial and Operations Engineering, College of Engineering, University of Michigan,
Ann Arbor, MI, United States, 4Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst,
MA, United States, 5Robotics User Experience and Industrial Design, Toyota Research Institute, Cambridge, MA,
United States, 6School of Information, University of Michigan, Ann Arbor, MI, United States
Pedestrians’ acceptance of automated vehicles (AVs) depends on their trust in the AVs.
We developed a model of pedestrians’ trust in AVs based on AV driving behavior and
traffic signal presence. To empirically verify this model, we conducted a human–subject
study with 30 participants in a virtual reality environment. The study manipulated two
factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk
type (signalized and unsignalized crossing). Results indicate that pedestrians’ trust in
AVs was influenced by AV driving behavior as well as the presence of a signal light.
In addition, the impact of the AV’s driving behavior on trust in the AV depended on
the presence of a signal light. There were also strong correlations between trust in
AVs and certain observable trusting behaviors such as pedestrian gaze at certain
areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also
present implications for design and future research.
Keywords: automated vehicles, human–automation interaction, trust in automation, virtual reality, implicit
communication
INTRODUCTION
Automated vehicles (AVs) that can drive without the supervision or intervention of a human driver,
i.e., SAE levels 4–5 (SAE-International, 2016) are becoming a reality. AVs have the potential to
reduce fossil fuel consumption, improve road safety, and provide greater access to transportation
(Litman, 2017). Yet, all these potential benefits depend largely on widespread public acceptance of
AVs. Despite the potential benefits of AVs, public skepticism over safety is still a major barrier to
AV acceptance (Liu et al., 2018; Xu et al., 2018; Zhang et al., 2019). This issue was also recently
witnessed in incidents where people harassed Waymo’s AVs because they felt uncomfortable and
unsafe around AVs1. These incidents and others demonstrate both the challenges and importance
of public acceptance of AVs.
To address this issue, scholars studying human–AV interactions have begun to examine the topic
of human trust in AVs (Verberne et al., 2012; Choi and Ji, 2015; Basu and Singhal, 2016; Ekman
et al., 2016; Du et al., 2019). However, much of this research has been directed at human–AV
interactions within the AV itself. This existing research focuses primarily on identifying and
examining the factors promoting drivers’ trust in AVs and the implications of drivers trusting AVs
1https://www.nbcnews.com/tech/innovation/humans-harass- attack-self- driving-waymo- cars-n950971

Jayaraman et al. Trust in AV at Crosswalks
(Gold et al., 2015; Verberne et al., 2015; Petersen et al., 2018;
Zhang et al., 2018; Du et al., 2019). Much less attention has
been paid to pedestrians and other road users outside of the
AV (Saleh et al., 2017). Nonetheless, AVs have to be accepted
by those who choose to ride in them as well as by pedestrians
and other road users outside the AVs. Owing to the vulnerability
of pedestrians in roadway interactions, there is now a growing
interest in studying pedestrian trust in AVs (Saleh et al., 2017;
Jayaraman et al., 2018).
Prior research on pedestrians’ interaction with human-
driven vehicles (HDVs) has highlighted the importance of non-
verbal communication to ensure safety (Sucha et al., 2017;
Rasouli and Tsotsos, 2018). Human drivers engage in non-
verbal communication via eye contact, facial expressions, and
hand gestures (Guéguen et al., 2015; Rasouli et al., 2018).
This is often done to communicate the drivers’ intent when
negotiating the right-of-way with pedestrians (Sucha et al.,
2017). In the absence of a human driver, it is not surprising
that pedestrians have expressed concerns over not knowing
or understanding the AV’s intention (Merat et al., 2018; Reig
et al., 2018). A clear understanding of the AV’s intention is thus
expected to foster trust in the AV and ultimately AV acceptance
(Saleh et al., 2017; Liu et al., 2018).
AVs can communicate their intent through explicit or
implicit means. Traditional methods of explicit communication
in HDVs include indicator lamps, brake lamps, and horns.
Current research on AV explicit communication primarily
explores the efficacy of additional specialized interfaces such
as light-emitting diode message boards, light-emitting diode
lights, interactive head lamps, etc., in conveying vehicle intent
in the absence of human drivers (Chang et al., 2017, 2018;
Habibovic et al., 2018; Mahadevan et al., 2018). Although these
approaches are valuable and insightful, there is currently no
one standard communication interface. Moreover, when the
number of AVs on the street increases in the future, explicit
communication may pose problems to other road users such as
information overload.
Implicit rather than explicit communication is a less
explored approach to tackling the communication challenge
and promoting trust between pedestrians and AVs (Dey and
Terken, 2017). Implicit vehicle communication refers to the
behavior cues derived from the vehicle’s driving (Ackermann
et al., 2018; Fuest et al., 2018). Pedestrians can get information
about the AV’s intent through its driving behavior, specifically
through its motion and kinematics (Pillai, 2017; Ackermann
et al., 2018). For example, an AV intending to yield the
right-of-way to pedestrians at the crosswalk will do so by
starting to slow down, whereas an AV that does not intend
to yield will not slow down or may even accelerate. In this
paper too, we use AV driving behavior to operationalize AV
implicit communication.
The intentions of AVs can also be understood from other
contextual elements such as traffic signals. AVs are expected
to be much more law-abiding than human drivers (Millard-
Ball, 2016; Meeder et al., 2017). Thus, under situations where
the right-of-way is clear, such as at signalized crosswalks, AVs
are expected to always follow the traffic rules and stop at the
red light. This law-abiding nature of AVs should help foster
pedestrians’ trust in the AVs. Conversely, in situations where
the right-of-way is unclear, pedestrians would be skeptical of
AVs. One such situation is an unsignalized crosswalk, where
the right-of-way varies from state to state in the United States
(Shinkle, 2016). In the case of signalized crosswalks, the traffic
signal clarifies the right-of-way to all traffic participants. As
the AVs are always expected to follow traffic rules, the traffic
signal indirectly informs the AVs’ intent to the pedestrians.
Traffic signals are a part of the infrastructure and dictate
the right of way. Thus, they can be considered as a higher
form of authority, and AVs could be expected to follow
the signal irrespective of their driving behavior. Thus, the
presence of a traffic signal might moderate the effects of
vehicle’s driving behavior on pedestrian trust. This interaction
between AV driving behavior and traffic signal, is relatively
less explored.
Existing research on pedestrian interaction with AVs or
HDVs has predominantly focused on understanding pedestrian
trust in vehicles from their behaviors such as willingness to
cross, crossing speed, looking behavior while crossing, etc.
(Rothenbücher et al., 2016; Rasouli et al., 2017) and use them
as proxies for trust. Trust is an attitude, which is related to
but different from these behaviors, which are actions. Behaviors
can be moderated by environmental, cognitive, and situational
factors (Lee and See, 2004). Thus, each of these different
behaviors can have a different relationship with trust. These
relationships between pedestrian trust and their behaviors are
relatively less studied.
This paper has two major contributions. First, it contributes
to the literature on pedestrian–AV trust by examining the
interaction effects of AV implicit communication and crosswalk
type on pedestrian trust in AVs. We consider implicit
communication in the form of the AV’s driving behavior:
aggressive, normal, and defensive. Second, we examine the
effects on both self-reported trust and trusting behavior
and examine the relationships between trust and trusting
behaviors. This study goes beyond other studies of pedestrian–
AV interaction by examining the impacts of the situation in
which the pedestrian–AV interaction takes place: unsignalized
and signalized (with traffic signals) crosswalks. To accomplish
this, we conducted a user study to investigate the moderation
effects of traffic signal on impact of AV driving behavior
on participants’ self-reported trust (i.e., attitude) and their
trusting behavior (i.e., actions). By measuring both self-
reported trust and trusting behaviors, we were able to
provide greater clarity with regards to their relationship
in the study of pedestrian–AV interactions. Overall, the
results provide new insights into trust between pedestrians
and AVs.
The rest of the paper is organized as follows. Section
Background and Related Work presents the background and
existing research on impacts of AV driving behavior and traffic
signal on pedestrians’ behavior and trust in AVs. Sections
Research Model and Method describe the proposed research
model and experimental methodology, respectively. Section
Results reports the results of a virtual reality user study.
Sections Discussion and Limitations discuss the implications and
limitations of the research, respectively.
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Jayaraman et al. Trust in AV at Crosswalks
BACKGROUND AND RELATED WORK
Although pedestrian interaction with HDVs has been studied
extensively (Schmidt and Färber, 2009; Schneemann and Gohl,
2016; Rasouli and Tsotsos, 2018), only recently have scholars
started to explore pedestrian interactions with AVs (Pillai, 2017;
Deb et al., 2018; Fuest et al., 2018; Jayaraman et al., 2018).
Research on implicit communications between pedestrians
and AVs has focused on the problems with the absence of the
human driver. The AV’s driving behavior has been used as a
form of implicit communication. Typically, researchers have
varied AV driving behavior from more to less aggressive by
varying the vehicle’s velocity profile and measured participants’
responses to the driving behavior (Pillai, 2017; Schmidt et al.,
2019). Studies have shown that AVs can implicitly communicate
their intent to pedestrians through their driving behavior (Fuest
et al., 2018; Schmidt et al., 2019). For example, Fuest et al.
(2018) examined AV intent recognition by pedestrians. They
used a “Wizard of Oz” (WOZ) approach where the driver
wore a car seat costume and hid in plain sight from the
pedestrians. Results indicate that pedestrians in general were able
to identify the AV’s intent of stopping or not stopping from its
driving behavior.
Scholars have also begun examining the impact of AV driving
behavior on pedestrian’s trust. Pedestrian trust in AVs is highly
relevant because pedestrians were more wary of crossing in front
of an AV without a driver than crossing in front of a HDV
(Lagstrom and Lundgren, 2015), indicating less trust in AVs. In
this paper, we use the definition of trust from Lee and See (2004)
that defines trust as “the attitude that an agent will help achieve
an individual’s goals in a situation characterized by uncertainty
and vulnerability.” In our study, trust is the attitude of the
pedestrians that the AV would help them in their goal to cross
the street.
Existing studies varied the AV driving behaviors and
explored pedestrian trust through behaviors such as
willingness to cross, crossing paths, and comfort of crossing
(Rothenbücher et al., 2016; Pillai, 2017; Zimmermann and
Wettach, 2017). For example, Rothenbücher et al. (2016)
explored the reactions of pedestrians upon encountering
an AV. They found that people generally crossed the street
normally and were tolerant of aggressive driving by the AV.
Pedestrians’ willingness to cross seemed to be unaffected
by the AV’s different driving behaviors. However, both
Pillai (2017) and Zimmermann and Wettach (2017) found
that when the AV engaged in what would be considered
more defensive driving behavior (decelerating early), vs.
more aggressive driving behavior (decelerating later), they
perceived the defensive AV to be more controlled and reliable
than the aggressive AV. Overall, there is more evidence
that different AV driving behaviors can affect pedestrian
trust differently.
To the authors’ knowledge, there are currently no studies
examining the role of the traffic signal on pedestrian’s trust
in AVs. However, several existing studies on pedestrian–HDV
interactions have shown that pedestrians express more trusting
behavior around signalized crosswalks. For example, Asaithambi
et al. (2016) found that pedestrians accepted shorter time gaps
and crossed closer to the vehicles after a traffic signal was installed
at an intersection. They also observed other trusting behaviors
such as reduced walking speed and increased waiting time after
the installation of the traffic signal, indicating more trusting
behaviors at signalized crosswalks. This finding agrees with
Tom and Granie (2011), who found that pedestrians were more
cautious of the oncoming vehicles and looked at vehicles more
during the unsignalized crosswalks than signalized crosswalks
before crossing the street. Similarly, Rasouli et al. (2017)
evaluated pedestrian communication behavior and found that
pedestrians are more cautious and less likely to cross the street
after communicating their intention (by looking at the oncoming
vehicles) if the crosswalk is not signalized and more likely to cross
if some form of signal is present. Overall, signalized crosswalks
are generally considered safer by pedestrians as they clarify the
right of-way, and thus, it can be expected that pedestrians would
exhibit more trusting behaviors around signalized crosswalks.
Nonetheless, we should acknowledge at least one study whose
findings contradict these results. Hatfield and Murphy (2007)
found that pedestrians exhibited more trusting behavior at
unsignalized crosswalks, in that they were more distracted and
did not pay attention to the traffic and the street while crossing at
unsignalized crosswalks than signalized crosswalks.
Although there is a fair understanding of the individual
effects of vehicle driving behavior and traffic signal on
pedestrian behavior, the interaction effect of the two is not
clearly understood. This is important especially in the case of
pedestrian–AV interactions as the absence of a human driver
may place more reliance on the traffic signal to understand the
AV intentions.
The relationship between trust as an attitude and the
observable trusting behaviors during pedestrian–vehicle
interactions is relatively unknown. Existing research has focused
on understanding pedestrian trust through their behaviors,
which we refer as trusting behaviors (Rothenbücher et al., 2016;
Pillai, 2017; Rasouli et al., 2017; Zimmermann and Wettach,
2017). Trust is related to but different from trusting behaviors.
Trust is an attitude, whereas trusting behaviors are actions. Azjen
(1980) developed a framework to clarify these differences, which
shows that behaviors result from intentions and intentions are
a function of attitudes. Trust in automation studies (Lee and
Moray, 1994; Riley, 1996; Lee and See, 2004) have identified
various factors affecting trusting behavior. Generally, trust is
only one of the factors that influence behaviors, in addition
to workload, situational awareness, system capability, and
other contextual and environmental factors (Lee and See,
2004). Although trust is related to trusting behaviors, the
relationship between trust and trusting behaviors might not
be straightforward.
Thus, existing research on pedestrian–AV interactions has
not clearly established the moderation effect of traffic signal
on the impact of AV driving behavior on pedestrian trust and
behavior. Furthermore, the relationships between pedestrian’s
subjective trust and their observable behaviors while crossing
a street is not well-known. In this study, we address both
of these shortcomings by conducting a human–subjects study
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Jayaraman et al. Trust in AV at Crosswalks
under different conditions of AV driving behavior with the
presence/absence of a traffic signal and measuring pedestrians’
trust and behaviors.
RESEARCH MODEL
Uncertainty and Pedestrians’ Trust in AVs
Uncertainty is defined as the inability to predict another’s
behavior because of a lack of information about the person or
environment (Baxter and Montgomery, 1996; Kramer, 1999).
When individuals meet, they communicate and exchange
information as a means of reducing uncertainty with regards
to each other’s intentions. The more information gained,
the less uncertainty one has about the other individual
or situation. However, when direct communication with
an individual is not possible, people seek information
from third parties or through observation (Sunnafrank,
1986).
As uncertainty increases, humans are more motivated
to engage in information seeking to reduce uncertainty.
Furthermore, uncertainty decreases as the amount of
information communicated increases. In other words, the
more the uncertainty, the more the people seek information to
reduce it; the more information provided, the less uncertainty.
Trust and uncertainty are inversely related (Lewis and Weigert,
1985; Colquitt et al., 2012). The greater uncertainty one has
about the outcome of an interaction with an agent, the less trust
one has in that agent (Robert et al., 2009; Colquitt et al., 2012).
Likewise, the more trust someone has in an agent, the less the
uncertainty regarding the outcome of an interaction with that
agent. Thus, availability of information plays an integral role in
improving trust by reducing uncertainty. For example, Helldin
et al. (2013) found that when the AV informed the uncertainty
in its ability to drive to the human driver, the trust in the AV
increased. In this paper, we consider the information about AV’s
intent to be available from the AV’s driving behavior and the
traffic signal. Unlike Helldin et al. (2013), we do not quantify
uncertainty but use the qualitative relationship between trust and
uncertainty from AV driving behavior and/or absence of traffic
signal to develop our hypotheses.
Hypotheses
In our research, we used uncertainty and availability of
information to understand a pedestrian’s trust in AVs. When
a pedestrian approaches a crosswalk, there is some degree of
uncertainty about an AV’s actions—Will the AV stop? If so, will it
stop within a safe distance, and will it remain stopped to allow the
pedestrian to cross safely? Pedestrians would attempt to reduce
this uncertainty by seeking information to help them predict the
AV’s actions. In our study, the information about the AV’s actions
can be directly estimated from the AV’s driving behavior and/or
can be obtained from the traffic signals which determine the right
of way for all traffic participants. The more information available
to facilitate the pedestrian’s prediction of the AV’s actions, the
less uncertainty and the more trust they should have in the AV.
Conversely, the less information available, the more uncertainty
and the less trust they should have in the AV.
Thus, this paper’s premise is:
As the available information that allows the pedestrian to predict
the actions of the AV increases, so should trust in the AV.
Our study included two sources of information: the AV’s driving
behavior and the traffic signal. Driving behavior is typically
classified into defensive, normal, and aggressive behaviors (Mizell
et al., 1997; Steimetz, 2008; Schneemann and Gohl, 2016).
Defensive driving is characterized as slow and predictable,
normal driving less so, while aggressive driving is characterized
by unpredictable behavior including high speeds, delayed
stopping, or not yielding the right-of-way (Mizell et al., 1997;
Steimetz, 2008). For example, a defensively driving AV might
sense a pedestrian trying to cross the street and could start to
slow down very early to indicate its intent of yielding to the
pedestrian, even though legally it may have been the AV’s right-
of-way. On the other hand, an AV driving more aggressively
might slow down and yield late or might even accelerate to
indicate that it is not yielding to the pedestrian. Thus, in
scenarios where a pedestrian walks onto the road, an aggressive
AV would brake later and harder than a defensive AV to
avoid a potential collision with the pedestrian (Zimmermann
and Wettach, 2017). This makes it hard to predict whether an
aggressive AV would ever slow down or stop for a pedestrian.
The unpredictability of aggressive driving should lead to low
trust in AVs. Thus, the more aggressive the vehicle drives,
the more uncertain its behavior, and the lower the trust
in AVs.
Pedestrians can also gather information from the
surroundings—road type, location of stop sign, traffic signal, etc.
Vehicles are expected to stop at traffic signals. Thus, the state of
the signal would provide information about what the vehicles
are expected to do. Particularly, AVs are expected to be more
law abiding, and thus, their intent would be more predictable.
Therefore, signalized crosswalks should decrease uncertainty
and increase AV trust by clarifying who should stop, whereas at
unsignalized crosswalks, the right of way is less clear (Shinkle,
2016).
Furthermore, the crosswalk type should moderate the impacts
of aggressive driving. The presence of a traffic signal should
reduce the negative impact of aggressive driving on AV trust.
Individuals should be more likely to believe that the AV will stop
regardless of its driving behavior. Therefore, aggressive driving
should have a weaker impact on AV trust at signalized crosswalks.
Finally, although the relation between trust and trusting behavior
may not be straightforward, we expect increased trust in the AVs
to generally result in more trusting behaviors. Simply put, the
more an individual trusts the AV, the more he or she should
engage in trusting behaviors with regard to the AV. Our research
model is graphically summarized in Figure 1.
We test the following hypotheses:
H1: Aggressive AV driving behavior decreases pedestrians’ trust
in AVs.
H2: Signalized crosswalk increases pedestrians’ trust in AVs.
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Jayaraman et al. Trust in AV at Crosswalks
FIGURE 1 | Pedestrian–automated vehicle (AV) trust model.
H3: Crosswalk type moderates impact of aggressive AV
driving behavior.
H4: Pedestrians’ trust in AVs engenders more trusting behaviors
from pedestrians.
METHODS
Study Participants
We recruited participants through email and obtained informed
consent from each participant. Thirty participants, of which
28 were college students, joined in this study (9 female),
with a mean age of 22.5 years [standard deviation (SD)
=2.8 years]. The study population was relatively young
as it appealed primarily to the student population in the
university, and we did not explicitly control for age during
subject recruitment.
Development of Experimental Apparatus
Participants were placed in an immersive virtual environment
(IVE) with an HTC Vive virtual reality headset (Vive; HTC
Corp., New Taipei, Taiwan), walking on an omni-directional
treadmill (Virtuix Omni; Virtuix Inc., Austin, TX); they took
on the role of a pedestrian walking in an urban environment.
The left side of Figure 2 shows the equipment setup, while
the right side shows the scene from participants’ point of view
as they wore the headset and walked on the treadmill. We
developed the urban scenario simulation to be as realistic as
possible. During the experiment, participants crossed a street at
a mid-block crosswalk with several oncoming AVs. The street
was one way with two lanes for the AVs. The AVs in this
study were fully automated without any humans inside and
produced engine sounds based on speed of the AV and distance
of the AV to the pedestrian. We manipulated the type of driving
behavior (defensive, normal, and aggressive) and the type of
crosswalk (signalized and unsignalized). We employed a within-
subjects experimental design so every participant experienced
all six conditions (3 ×2). Sample videos of the six treatment
conditions are available online2for reference. The IVE was
built using the Unity Game Engine (Unity Technologies, San
Francisco, CA). The treadmill senses feet movements and torso
orientation to provide a direction and speed for movement in the
virtual environment that matches the participant’s input in the
physical world.
Experimental Task
In the experiment, participants were asked to move three
numbered balls, one at a time, from one side of the street
to the other, placing them in corresponding numbered boxes.
Participants were required to remember the ball’s number,
which disappeared after it was picked up. The ball task was
designed such that the crossing activity was embedded within
the overall task of moving the balls. This served two purposes.
First, it allowed participants to make multiple street crossings
without experimenters explicitly instructing them to make
such crossings. Second, the task was designed to reduce any
participant reactivity, such as from an observer effect, wherein
participants’ actual crossing behaviors could be affected by their
knowledge that the experimenters were specifically measuring
such behaviors (Baum et al., 1979). This ball task helped disguise
from the participants the fact that their crossing behaviors were of
primary interest to the experimenters. The activities for moving
a ball include approaching the crosswalk, waiting to cross,
crossing the street, approaching the ball, picking the up the ball,
approaching the crosswalk, waiting to cross, crossing the street,
approaching the boxes, and dropping the ball. The numbers in
Figure 3A describe a typical sequence of pedestrian movements.
Thus, by performing the ball-task, they had to cross the street
six times.
Design of Interaction Scenarios
The interaction scenarios were designed to mimic a downtown
urban crosswalk. Figure 3A shows the layout of the virtual reality
environment. Participants could move around all the different
2https://drive.google.com/drive/u/1/folders/1jvKOELsDp8Bj4Sc
JcOmPpKMGZeRosycy
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Jayaraman et al. Trust in AV at Crosswalks
FIGURE 2 | Virtual reality setup for user study. The left side shows the user wearing the HTC Vive headset and walking on the omni-directional treadmill. The right side
shows the virtual environment as seen by the participant.
FIGURE 3 | (A) Pedestrian state divisions in the virtual environment. Numbered arrows indicate a typical pedestrian path while doing the task. (B) Driving profiles for
the three driving behaviors when the pedestrian is on the road in the same lane as the automated vehicle (AV). To achieve the specified stopping distance and slow
speeds, defensive behavior decelerated much earlier than normal or aggressive.
areas including the sidewalks, the road lanes, and the wait areas.
The wait areas are where the pedestrian would typically wait
before crossing the road. Participants encountered AVs while
crossing in either direction.
In both signalized and unsignalized conditions, all AVs
approached the crosswalk at a constant speed of 15.6 m/s (35
mph). For each treatment condition, the vehicle changed its
driving behavior when it encountered a pedestrian within its
reaction distance (refer to Table 1). This distance signifies the
attentiveness of the different driving behaviors. As discussed
earlier, the unpredictability of aggressive driving can be attributed
to the delayed stopping or failing to yield the right of way
(Mizell et al., 1997) by the AV. We defined the aggressive
driving behavior to be less cautious, by which we made AVs
with aggressive driving behavior react to pedestrians much
later than the defensive or normal driving conditions. We
defined this behavior by varying the reaction distance, which is
the distance from the pedestrian the AV would start reacting
to pedestrians. We also varied the AVs’ reactions to the
pedestrian such as stopping, slowing down, or not slowing
based on the position of the AVs and the pedestrian. The
different driving behaviors were obtained by tuning the AVs’
reactions, reaction distance, and driving parameters such as
acceleration and speed across the three behaviors. The resulting
behaviors were perceived to be different from one another
during internal validation. The change in vehicle behavior is
based on the discrete location of the pedestrian as described
in Table 1. The stopping distance in Table 2 refers to the
distance between the pedestrian and the vehicle when it
is stopped.
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Jayaraman et al. Trust in AV at Crosswalks
TABLE 1 | Different vehicle reactions to various pedestrian positions characterizing the different driving behaviors.
Behavior Pedestrian position Reaction distance (m)
Sidewalk Wait area Same lane as
vehicle
Other lane as
vehicle
Defensive Full speed Slow speed Stop Stop 50
Normal Full speed Slow speed Stop Slow speed 30
Aggressive Full speed Full speed Stop Full speed 10
TABLE 2 | Vehicle parameters characterizing the different driving behaviors.
Behavior Stopped
distance
(m)
Maximum
acceleration
(m/s2)
Slow
speed
(m/s)
Full
speed
(m/s)
Defensive 3 3 4 15.6
Normal 2 5 7 15.6
Aggressive 1 8 NA 15.6
The cars always stop before the crosswalk if there is a
pedestrian on the street. The cars do not stop when pedestrians
are waiting/walking on the sidewalk. However, to elicit realistic
pedestrian behavior, participants were not explicitly told that AVs
would always stop if they are on the road. Furthermore, the AVs
with the same driving behavior do not react in the exact same
way each time as their deceleration rates depend on the relative
position between the pedestrian and AVs when the pedestrian
reach the particular positional states.
Table 1 provides the discrete AV driving behavior model based
on the pedestrian’s positional state, and Table 2 provides the
vehicle parameters used in the study. Typical driving profiles
for the three driving behaviors are shown in Figure 3B. For a
pedestrian in the wait area, during the normal driving behavior
conditions, the vehicles in the near lane and far lane slowed down
from 15.6 to 7 m/s. They started slowing down at a distance
of 30 m from the pedestrian. When the pedestrian stepped into
the near lane, the vehicle in near lane stopped 2 m from the
pedestrian, whereas the vehicle in far lane continued at a speed
of 7 m/s.
In addition, in the signalized conditions, the AV stopped at the
appropriate stopping distances (Table 2) when the signal was red
or yellow and maintained the same behavior as in unsignalized
conditions (Table 1) when the signal was green. In signalized
conditions, when the pedestrian was not on the road, the stopping
distance refers to the distance between the vehicle when it was
stopped and the center of the crosswalk. The behaviors across
signalized and unsignalized conditions were maintained to be
as similar as possible for experimental validity and to avoid any
confounding effects due to variations in the vehicle behaviors,
when examining the effect of traffic signal.
The signal for the vehicles operated on a 38 s cycle: green
for 20 s, yellow for 3 s, then red for 15 s (Urbanik et al., 2015).
The cycle ran continuously on the background, but the signal
changed to yellow and red only after the participant pressed the
provided signal button. If the signal button was not pressed by
TABLE 3 | Survey measurement validity using factor and cross loadings of trust
and simulator sickness survey measures.
Item Self-reported AV trust Simulator sickness (SS)
Trust: competence 0.83 0.12
Trust: predictability 0.86 0.01
Trust: dependability 0.86 0.09
Trust: responsibility 0.84 0.05
Trust: reliability 0.62 0.02
Trust: faith 0.72 0.00
SS: disorientation 0.07 0.91
SS: nausea 0.06 0.82
SS: oculomotor 0.01 0.91
Convergent validity: factor loadings >0.7; Discriminant validity: cross loadings <0.3.
Bold values indicate the representative factors included in the measures in each column.
the participant, the signal remained green. Vehicular traffic was
generated in a predetermined pseudo-random sequence of short
(3 s) and long (5s) gaps. The probability of a short gap occurring
was 75%, and a long gap was 25%, inducing the participants to
observe the cars during the short gaps while waiting for a long
gap to occur to cross. Vehicles were generated in both lanes of
the street, going in the same direction.
Training
Participants underwent two training sessions before the start of
testing. In the first training session, there were no vehicles on
the road and participants practiced the task of picking the balls
from one side of the road and placing them on a receptacle on
the other side, until they were comfortable doing the task. In
the second training session, participants were fenced so that they
could not be able to enter the road but see the AVs on the road.
In this session, AVs were shown to pedestrians so that they can
see how the AVs looked like and not be surprised when they see
them during the actual scenarios. AVs with a constant speed of
35 mph (15.6 m/s) were traveling on the road but did not react
to the pedestrians. However, the behaviors of the AVs during the
actual treatment conditions followed the behaviors defined by the
parameters in Tables 1,2.
We employed a within-subjects experimental design. After the
training sessions described above, participants experienced each
of the six treatment conditions (defensive, normal, and aggressive
driving behaviors for each of signalized and unsignalized
crosswalks) once. The conditions’ sequence was counterbalanced
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Jayaraman et al. Trust in AV at Crosswalks
using a Latin square design (Lewis, 1989). The standard Latin
square design that we employed in the study is available in
Appendix 1.
The balanced Latin square design has a group of sequences
of treatment conditions such that every condition appears before
and after every other condition exactly once. This design helps to
compensate for immediate sequential effects (Lewis, 1989).
Measurements
We collected attitudinal, behavioral, physiological, and other
self-reported measures. We measured participants’ propensity
to trust (Preusse and Rogers, 2016) and experience with virtual
reality before the experiment (calculated as a mean of 1–
7 Likert scale responses). After each treatment condition,
participants gave 1–7 Likert scale ratings measuring trust
and perceived AV aggressiveness. For measuring self-reported
trust, we adapted the Muir scale questions (Muir, 1987), a
highly validated trust in automation scale. We modified the
questions to reflect the pedestrian–AV interaction context (refer
Appendix 2). Self-reported trust was calculated as the mean
of the responses to the trust questionnaire. We also measured
simulator sickness, calculated using the items and procedure
mentioned in Kennedy et al. (1993) (refer Appendix 3), at the
end of the experiment.
We collected six dependent measures of trusting behavior
from the simulation, some of which were calculated for each
of the six crossings within a treatment condition and averaged.
Average distance to collision measured how close a participant
was to being hit by the AV as the distance between the AV
in its lane and the participant when he/she entered that lane.
Average jaywalking time was the average time participants spent
either crossing the street when the AV had the right of way,
which was whenever the pedestrian signal was red in the
unsignalized conditions, or crossed the street outside of the
crosswalk in both the signalized and unsignalized conditions.
Average wait time measured the average time they spent
waiting before they crossed the street. Average crossing speed
measured how fast they crossed the street. Average crossing
time was the average duration of the crossing. Overall task
time measured how long they took to complete the entire
treatment condition.
We examined participants’ eye gaze to explore its relationship
with self-reported AV trust. A lack of monitoring is related
to high trust in automation (Hergeth et al., 2016). We
divided the environment into seven areas of interest (AOI):
(1) looking at approaching AVs, (2) checking for AVs, (3)
pedestrian signal light, (4) traffic light, (5) task materials, (6)
crosswalk and buildings directly across the crosswalk, and (7)
everything else in the environment that included the sky, other
buildings along the road, and roads not in the crosswalk
region. The crosswalk and buildings directly across represented
regions when a participant stared ahead. We measured the
duration of time each participant spent looking at the different
AOI using the Pupil Labs eye tracker (Pupil; Pupil Labs,
Berlin, Germany).
Other research studies have performed post-hoc frame-by-
frame manual coding to identify the AOI (Tapiro et al.,
2014; Trefzger et al., 2018). In our study, the AOI at
which the participants gazed was identified in real time
by interfacing the Pupil Labs eye tracker with the Unity
simulation. At every sampling instant, the Unity simulation
obtained the gaze point from the eye tracker and cast a
ray to automatically identify which AOI intersected with
the gaze ray.
TABLE 4 | Descriptives of measurements and correlations between the measurements.
Parameters Mean SD 1 2 3 4 5 6 7
1 Trust 5.68 1.10
2 Aggressive
driving
3.47 1.83 −0.47**
3 Signalized
crosswalks
0.50 0.50 0.33** −0.31**
4 Driving condition 0.50 0.50 −0.08 0.28** 0.00
5 Age 22.50 2.76 0.06 −0.06 0.00 0.00
6 Propensity to
trust
5.33 0.46 0.20** −0.31** 0.00 0.00 −0.14
7 Virtual reality
experience
3.36 1.20 0.01 −0.01 0.00 0.00 −0.34** 0.20**
8 Simulator
sickness
28.30 23 −0.10 0.31** 0.00 0.00 0.22** −0.42** −0.24**
**Correlation is significant at the 0.01 level (two-tailed).
Trust is calculated as the mean of the responses (1–7 likert scale) from the survey in Appendix 1.
Aggressive driving is a rating of perceived aggression (1–7 Likert scale) of the AV driving.
Signalized crosswalks is a boolean variable for presence/absence of traffic signal.
Driving condition is a boolean variable for low-aggressive and high-aggressive behavior.
Propensity to trust is calculated as the responses (1–7 likert scale) from the Complaceny rating survey (Preusse and Rogers, 2016).
Virtual Reality experience is measured on a 1–7 Likert sclae before the experiment starts.
Simulator sickness is calculated using the formula in Kennedy et al. (1993).
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Jayaraman et al. Trust in AV at Crosswalks
RESULTS
The descriptives (mean and standard deviation) of our survey
measurements are reported in Table 4. Having a within-subjects
experimental design, our study collected repeated measurements
(for each of the six treatment conditions) from the same subject.
To account for this non-independence in the data, we employed
mixed linear repeated modeling (MLRM) technique (Stroup,
2012) to understand the relationships between the dependent
and independent variables. MLRM makes it easy to study the
effects of covariates in addition to the treatment variables on the
dependent variable. We used SPSS v24 (IBM, Armonk, NY) for
all our analyses. The data used for the analyses and the SPSS
analyses codes are available as Supplementary Materials.
Manipulation Check of Aggressive Driving
We conducted a manipulation check to verify if the participants
perceived each of the driving conditions to have different levels
of driving aggression. To accomplish this, we ran a MLRM with
driving condition as the independent variable and the perceived
AV driving aggression as the dependent variable. The model
revealed a significant difference (p<0.001) among the driving
conditions. The mean (standard deviation) values were x=2.67
(0.22) for defensive, x=3.44 (0.21) for normal, and x=4.24
(0.23) for aggressive driving conditions. As shown in Figure 4,
all pairwise comparisons were significantly different from one
another (p<0.05). Our results indicate that our manipulation
of driving behavior was successful.
Measurement Validity
To verify if our survey constructs measured what they were
intended to measure, we conducted a factor analysis to examine
convergent and discriminant validity of the self-reported trust
and simulator sickness measures (see Table 3). Only one item
(Trust: Reliability) did not meet the 0.7 loading requirement
indicating convergence validity. In addition, no crossloadings
exceeded 0.3, indicating discriminant validity. Thus, the results
indicate both discriminant and convergent validity (Fornell and
Larcker, 1981).
To maintain content validity and consistency with previous
studies, we included the item with the low factor loading of 0.62.
In addition, reliabilities of both self-reported trust (α=0.92) and
simulator sickness (α=0.85) exceeded the 0.7 recommendation
(Carmines and Zeller, 1979). Low correlations were observed
among all the measured variables. This provided evidence
of discriminant validity among the variables. Specifically, all
correlations were below 0.5 (see Table 4).
Before conducting our analyses, we checked for
heteroscedasticity by performing Glejser test, which states
that variables have non-linear and unequal variances if p<
0.05 (Glejser, 1969). We found evidence of both non-linearity
and unequal variances related to average distance to collision
(p=0.01), average jaywalking time (p=0.03), and average
crossing speed (p=0.01). To improve the linearity and equality
of the variances, we performed log transformations on each
dependent variable and verified the absence of heteroscedasticity
(p≥0.05 for all dependent variables).
Population Effects
We found that neither age (fixed effects estimate, β=0.03,
p=0.77) nor gender (fixed effects estimate, β= −0.22, p=
0.40) had significant effects on the self-reported trust in AVs. The
effect of age was not significant perhaps because of the limited
age range of our study population. The study had a fairly young
FIGURE 4 | Manipulation check of aggressive driving. Perceived aggression of automated vehicle (AV) driving is lowest for defensive driving and highest for aggressive
driving conditions.
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Jayaraman et al. Trust in AV at Crosswalks
population (18–30 years) with a mean age of 22.5 years [and
standard deviation (SD) =2.8 years].
Hypothesis Testing
For our analysis, we used the self-reported perceived AV
aggression as an independent variable because it is a more
accurate measure of how the pedestrians actually perceived
the AVs’ behavior, which in turn would affect their trust. We
used the self-reported trust, calculated as the mean of the
responses to the trust survey, as the dependent variable. We
conducted the analysis for H1–H3 in two parts. First, we analyzed
the main effects model with the control variables and the
variables measuring aggressive driving (self-reported perceived
AV aggression) and crosswalk type. Second, we included the
moderation effect involving signalized crosswalks and aggressive
driving. We employed the full model with the moderation effect
because it had a lower Schwarz’s Bayesian information criterion
(=472) than the model with only the main effects (Schwarz’s
Bayesian information criterion =500) and thus fit the data better
(Stone, 1979). The full model and correlations are shown in
Tables 4,5.
We derived our mixed linear model from both level 1
(Equation 1) and level 2 (Equation 2) equations (Hoffman and
Rovine, 2007). This two-level modeling accounts for the effects
of both group-level and individual-level variables and allows
random variations for the group-level variables (Stroup, 2012).
In the level 1 equation, Yij is the trust outcome of individual
i(from 30 subjects) in group j(from 6 treatment conditions).
β0jrepresents the group intercept values, β1jand β2jrepresent
the effects of group predictors SignalCondj and DriveCondj,
respectively, whereas β01,β02 ,β03,β04 , and β05 represent the
effects of the individual predictors Aggressij, Ageij,ProTrustij,V
irReaExpij, and SimSicij, respectively. εij represents the residual
for individual iin group j.
Yij =β0j+β1j(SignalCondj)+β2j(DriveCondj)
+β01(Aggressij)+β02 (Ageij)+β03(ProTrustij)
+β04(VirReaExpij)+β05(SimSicij)+εij (1)
Group level variables are associated with varying intercepts
shown in the level 2 equations (Equation 2). Gammas γ00,
γ10, and γ20 represent the intercepts (fixed main effects),
while ν0j,ν1j, and ν2jrepresent their corresponding variances.
TABLE 5 | Trust model: higher trust during less aggressive driving and during presence of signal with presence of signal moderating the effect of aggressive driving on
trust.
Independent parameter Estimation (β) SE df t Sig. 95% CI
Intercept* (γ00) 4.93 0.23 63.76 21.86 0.00 4.49 5.39
Aggressive driving* (γ01)−1.08 0.22 76.96 −4.97 0.00 −1.51 −0.65
Signal condition* (γ10) 0.41 0.12 85.16 3.50 0.00 0.18 0.65
Aggressive driving ×signal
condition* (γ11)
0.40 0.12 91.67 3.43 0.00 0.17 0.64
Driving condition (γ20) 0.10 0.06 49.68 1.76 0.08 −0.01 0.21
Age (γ02)−0.01 0.12 21.47 −0.08 0.93 −0.27 0.25
Propensity to trust (γ03) 0.11 0.13 21.41 0.88 0.39 −0.15 0.38
Virtual reality experience (γ04)−0.01 0.12 21.29 −0.03 0.97 −0.26 0.25
Simulator sickness (γ05) 0.03 0.13 22.72 0.21 0.83 −0.24 0.30
Independent parameter Estimation (β) SE Wald ZSig. 95% CI
Random intercept variances (ν0j) 0.21 0.14 1.49 0.14 0.06 0.77
Random signal condition variances (ν1j) 0.03 0.03 0.99 0.32 0.00 0.24
Random driving condition variances (ν2j) 0.01 0.02 0.64 0.52 0.00 0.21
Residual variances (εij)
Treatment condition 1 (Defensive
Unsignalized)*
0.98 0.28 3.52 0.00 0.56 1.71
Treatment condition 2 (Normal
Unsignalized)*
0.52 0.16 3.35 0.00 0.29 0.94
Treatment condition 3 (Aggressive
Unsignalized)*
1.14 0.33 3.44 0.00 0.65 2.02
Treatment condition 4 (Defensive
Signalized)*
0.21 0.08 2.77 0.01 0.11 0.44
Treatment condition 5 (Normal
Signalized)*
0.31 0.09 3.38 0.00 0.01 0.44
Treatment condition 6 (Aggressive
Signalized)
0.07 0.06 1.10 0.27 0.01 0.39
*Significant model parameters.
Fixed effects estimates (β) indicate the direction and degree of relationship between trust and the model variables.
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Jayaraman et al. Trust in AV at Crosswalks
TABLE 6 | Mixed linear models of trust and each trusting behavior separately with trust being the dependent variable predicting trusting behaviors.
Parameter Estimation (β) SE df t Sig. 95% CI
Intercept 0.88 0.01 75.63 87.43 0.00 0.86 0.90
CL trust −0.38 0.01 68.60 −38.71 0.00 −0.40 −0.36
Dependent Variable: Log of average distance to collision* (m)
Intercept −0.08 0.04 17.37 −1.79 0.09 −0.17 0.01
CL trust 0.17 0.04 13.84 3.78 0.00 0.07 0.26
Dependent Variable: Log of average jaywalking time* (s)
Intercept 16.24 0.53 92.74 30.60 0.00 15.19 17.30
CL trust 4.26 0.54 63.37 7.92 0.00 3.19 5.34
Dependent Variable: Average waiting time* (s)
Intercept 222.58 4.33 132.34 51.45 0.00 214.02 231.14
CL trust 32.31 4.24 75.75 7.63 0.00 23.88 40.76
Dependent Variable: Overall task time* (s)
Intercept 3.86 0.10 165.42 39.40 0.00 3.67 4.06
CL trust −0.08 0.10 105.36 −0.82 0.42 −0.28 0.12
Dependent Variable: Average crossing time (s)
Intercept 0.27 0.01 173.80 32.28 0.00 0.25 0.29
CL trust 0.01 0.01 108.91 0.70 0.49 −0.01 0.02
Dependent variable: Log of average crossing speed (m/s)
CL trust =Condition level trust, mean of trust for each treatment condition.
All six trusting behaviors are measured from the simulation.
Fixed effects estimates (β) of the models indicate the direction and degree of relationship between trust and trusting behaviors.
*Behaviors with significant relationship with trust.
FIGURE 5 | Main effects of signalized crosswalks. Higher self-reported AV
trust in signalized conditions.
These variances highlight that β0j,β1j, and β2jare allowed to
randomly vary. Gammas γ01,γ02 ,γ03,γ04 , and γ05 represent the
intercepts (fixed main effects) for their corresponding individual-
level counterparts, whereas γ11 represents the effect of the
interaction term Aggressij ∗SignalCondj.β01,β02,β03 ,β04, and
β05 are not allowed to randomly vary and therefore do not have
corresponding variances.
β0j=γ00 +ν0j
β1j=γ10 +γ11(Aggressij)+ν1j
β2j=γ20 +ν2j
β01 =γ01
β02 =γ02
β03 =γ03
β04 =γ04
β05 =γ05 (2)
Our mixed linear model is derived by substituting Equation
(2) into Equation (1). The final model we used is shown in
Equation (3).
Yij =γ00 +γ01(Aggressij)+γ02 (Ageij)+γ03(ProTrustij)
+γ04(VirReaExpij)+γ05(SimSicij)+γ10(SignalCondj)
+γ11(Aggressij)∗(SignalCondj)
+γ20(DriveCondj)+ν0j+ν1j(SignalCondj)
+ν2j(DriveCondj)+εij (3)
We also tested H4 using a mixed linear modeling approach
(Table 6). We used the mean of trust in the AV per condition
as the independent variable when predicting trusting behaviors.
To justify the aggregation by condition, we calculated the
intraclass correlation coefficient (ICC). ICC measures the degree
to which an individual level variable is influenced by group
level membership. The higher the ICC, the more the individual-
level variable is driven by group membership, and the more
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Jayaraman et al. Trust in AV at Crosswalks
justification one has to create a group-level variable. The ICC
value of trust in the AV per condition was 0.44, exceeding 0.10
(Bliese, 2000), indicating that a group variable was valid.
H1 posited that aggressive driving decreases trust in the AV;
this was supported (fixed effects estimate, β= −0.17, p<0.001).
H2, which stated that signalized crosswalk increases trust in
the AV, was supported (fixed effects estimate, β=0.53, p<
0.001). Figure 5 shows the main effect of the signalized crosswalk
vs. unsignalized crosswalk on trust in the AV. Finally, H3 was
examined in the full model (Table 5). H3, the impact of aggressive
driving on trust depends on the type of crosswalk, was also
supported (fixed effects estimate, β=0.38, p<0.001) shown
in Figure 6.
H4, which stated that trust in the AV leads to more trusting
behaviors, was partially supported. We defined trusting behavior
as behavior that prolongs a participant’s exposure to being
vulnerable to the AV’s actions. Therefore, when participants
trusted the AV, we expected participants to cross closer to AVs
resulting in decreased average distance to collision, cross earlier
resulting in decreased wait time and overall task time, and walk
slowly resulting in decreased crossing speed. We also expected
the participants to take more risks and cross when it was not their
right-of-way resulting in increased jaywalking time and increased
crossing time due to decreased crossing speed. We employed an
MLRM for each of these objective measures of trusting behavior
with the objective measure being the dependent variable and
self-reported trust being the independent variable.
Trust in the AV was significantly related to average distance to
collision (fixed effects estimate, β= −0.38, p<0.001), average
jaywalking time (fixed effects estimate, β=0.17, p<0.05),
average waiting time (fixed effects estimate, β=0.18, p<0.001),
and overall task time (fixed effects estimate, β=32.31, p<0.001).
In other words, the more participants trusted the AV, the closer
they came to the AV while crossing, the more they jaywalked, the
longer they waited to cross and more time it took for them to
complete the task. Trust in the AV was not related to average
crossing time (fixed effects estimate, β= −0.08, p>0.05) or
average crossing speed (fixed effects estimate, β=0.02, p>0.05;
see Table 6).
Following previous literature, a lack of visual monitoring of
the automation can also be viewed as an act of trusting behavior
(Hergeth et al., 2016). Therefore, we expected that trust in AVs
would negatively correlate with gaze at AVs. This could be an
indication that participants were not concerned about being
hit by the AV. To better understand the relationship between
monitoring and trust, we divided our analysis by one of three
actions: waiting, crossing, and tasking. Waiting included the
time a participant spent waiting to cross the street. Crossing
included the actual walking across the street. Tasking included
the remaining time spent working on the task of moving
the balls. We calculate gaze ratios per action by dividing the
duration a participant focused on a particular area by the action
type’s total duration (Table 7). Then, we conducted a repeated
measure correlation between each gaze area ratio and trust in the
AV (Table 8).
Trust in the AV was negatively related to monitoring. Time
spent looking at the approaching AVs while crossing was
negatively correlated with trust in the AV for normal and
aggressive driving. In addition, there was negative correlation
between self-reported AV trust and time spent checking for
AVs while crossing in normal driving behavior. Looking at
the pedestrian light while crossing in normal behavior and
looking at traffic light while waiting and tasking in defensive
behavior were negatively correlated with self-reported AV trust.
These results support previous research suggesting that decreased
monitoring is related to increased trust (Hergeth et al., 2016).
While waiting at the crosswalk, gaze at the crosswalk and the
buildings across the crosswalk indicate that the pedestrians were
staring ahead and not monitoring the AVs. This time spent
looking at the crosswalk and buildings positively correlated
with trust.
DISCUSSION
In this study, we proposed hypotheses for the development of
trust based on information availability. When two agents are
interacting, the more information gained about the other agent,
the less uncertain one is about the other agent. We highlight
the importance of AV driving behavior and traffic signal and the
moderation effect of traffic signal on the impact of aggressive
driving on pedestrians’ trust in AVs. Specifically, we found that
both sources of information, AV driving behavior and traffic
signal, predicted pedestrians’ trust in the AVs.
We systematically examined AV driving behavior and
found that aggressive AV driving behavior significantly
decreased AV trust. Thus, driving behavior could implicitly
convey the AV intent to pedestrians. This finding
aligns with existing research that has found pedestrians
generally prefer a conservative AV driving behavior
(Pillai, 2017; Ackermann et al., 2018; Fuest et al., 2018).
Our study also calls attention to the importance of the
presence of traffic signals in pedestrian–AV interactions. To the
authors’ knowledge, impact of traffic signal on pedestrian trust in
AVs has not been explored before. We found that pedestrians,
in general, trusted the signalized crosswalks more than the
unsignalized crosswalks. This is in line with existing research
in pedestrian–HDV interactions, which have reported increased
trusting behavior such as lower crossing speeds, reduced gaze
at vehicles, and shorter distances to collision at signalized
crosswalks (Tom and Granie, 2011; Asaithambi et al., 2016;
Rasouli et al., 2017).
More importantly, we found that influence of the AV’s driving
behavior is largely determined by whether the crosswalk is
signalized or unsignalized. Signalized crosswalks significantly
reduced the negative effects of aggressive driving on trust. It
could be because signalized crosswalks dictate the right of way,
and AVs are expected to follow the right-of-way (Meeder et al.,
2017). Thus, the AVs, irrespective of their driving behavior, are
always expected to stop when the pedestrian has the right-of-
way. In any case, our findings demonstrate the importance of
incorporating the presence of traffic signal when understanding
trust in the AV and help to identify generalized situations during
which pedestrians trust AVs. For example, trust is generally high
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Jayaraman et al. Trust in AV at Crosswalks
FIGURE 6 | Moderation of aggressive driving by signalized crosswalks. Trust reduction due to high aggression behavior is lower for signalized than unsignalized
crosswalks.
TABLE 7 | Gaze distribution by areas of interest (AOI) and driving behavior condition.
AOI Defensive (%) Normal (%) Aggressive (%) Overall (%)
AVs approaching the
crosswalk
25.0 18.1 13.4 18.7
Checking for AVs
(looking in the general direction of AVs
when no AVs are present on the road)
2.0 2.3 4.4 2.9
Crosswalk and
buildings across the crosswalk
35.9 38.0 40.6 38.4
Task elements (racks on
either side of crosswalk)
11.5 12.2 13.2 12.3
Pedestrian signal light
on either side of crosswalk
3.4 3.5 3.7 3.6
Traffic light 0.7 0.7 0.4 0.6
All other areas 21.5 25.2 24.3 23.5
in signalized conditions irrespective of the driving behavior (refer
to Figure 6).
This study highlights the link between trust in the AV
and trusting behaviors. We hypothesized that trust in the
AV increases trusting behaviors related to the AV (H4). Our
findings related to trusting behaviors fall into three categories.
The first category confirms our initial hypothesis. When
pedestrians undertrusted the AVs, they exhibited behaviors such
as high distance to collision, fewer instances of jaywalking,
more looking at AVs while crossing, etc. As trust in the
AV increased, pedestrians were much more willing to be
vulnerable to the actions of the AV, which came in the
form of reductions in distance to collision and increases
in jaywalking. We also observed trusting behavior in the
form of a lack of monitoring the AVs (i.e., low gaze ratio
at the AVs when the self-reported trust scores were high),
which aligns with existing research on drivers’ trust in
AVs (Hergeth et al., 2017). Pedestrians were more willing
to place themselves in harm’s way when they trusted the
AV. However, this behavior can also be unsafe and lead
to injury, exemplifying the issue of overtrust. Thus, by
examining pedestrian trusting behaviors, our study calls
attention to trust calibration of pedestrians for safe interactions
with AVs.
However, the second category of trusting behaviors was
contrary to our expectations. Increases in trust in the AV led
to increases in overall task time and average wait time. One
interpretation of our results is that the more pedestrians trusted
the AV, the less worried and hurried they were to complete the
task. In this sense, increases in overall task time and waiting times
might be expected. The third category refutes our hypothesis.
Trust in the AV did not have an impact on total crossing time
or average crossing time. Similarly, crossing speed was found
to be unrelated to trust in the AV. Nevertheless, overall, our
study highlights the link between trust and trusting behaviors
regarding AVs. Behavioral measures such as distance to collision,
jaywalking time, and looking at AVs are more indicative of
pedestrian’s trust in AVs. This agrees with existing studies that
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Jayaraman et al. Trust in AV at Crosswalks
TABLE 8 | Repeated measures correlation between gaze and trust separated by activity and driving condition.
Waiting Crossing Tasking
Areas of interest Defensive Normal Aggressive Defensive Normal Aggressive Defensive Normal Aggressive
AVs approaching the crosswalk −0.07 −0.24 −0.13 −0.23 −0.34* −0.25* −0.03 −0.03 0.00
Checking for AVs (looking in the
general direction of AVs when no AVs
are present on the road)
−0.22 −0.11 −0.13 −0.16 −0.34* −0.24 −0.16 0.13 −0.13
Crosswalk and buildings across the
crosswalk
0.24* 0.42*** 0.27** 0.13 0.38** 0.20 0.19 0.44*** 0.19
Task elements (racks on either side
of crosswalk)
0.01 −0.04 −0.12 0.07 0.05 −0.12 0.00 −0.08 −0.14
Pedestrian signal light on either side
of crosswalk†
−0.02 −0.03 0.02 −0.13 −0.22* 0.05 −0.07 −0.04 0.02
Traffic light†−0.20* −0.14 −0.02 −0.02 0.04 −0.07 −0.20* −0.17 −0.06
All other areas −0.15 −0.32* −0.31* −0.10 −0.37** −0.19 −0.07 −0.30* −0.14
†Pedestrian signal light and traffic light AOI available only during the three signalized conditions.
A mixed linear model fitted between trust and each gaze ratio to calculate the repeated measures correlation.
*Correlation is significant at the 0.05 level (two-tailed).
**Correlation is significant at the 0.01 level (two-tailed).
***Correlation is significant at the 0.001 level (two-tailed).
have used similar measures to indicate pedestrian trust (Tom and
Granie, 2011; Asaithambi et al., 2016; Rasouli et al., 2017).
The link between certain trusting behavior and self-reported
trust identified in this study facilitates real-time measurement of
trust in AVs. Measures such as gaze ratio at AVs while waiting and
waiting times are trusting behaviors exhibited before the actual
start of crossing. These behaviors could be used by the AVs to
estimate pedestrians trust in the AVs, which in turn could be used
to moderate the driving behavior of AVs to calibrate pedestrian
trust in the AVs. For example, if pedestrian trust is estimated
to be high, the AV can exhibit an aggressive driving behavior to
reduce trust, and when the trust is estimated to be low, the AV
can exhibit a defensive driving behavior to improve the trust in
the AVs.
Existing studies (Pillai, 2017; Zimmermann and Wettach,
2017) mostly employed predetermined velocity profiles and thus
were not reactive to pedestrians. In these studies, vehicles were
perceived to be reactive to participants because participants
were always placed close to the road at a ready-to-cross
position. Our study, however, employed a reactive driving
behavior model for the AV based on discrete pedestrian
positional states (refer to Table 1). This reactive behavior is
more similar to how real-life interactions between vehicles
and pedestrians would take place. For example, the AV slows
down only when the pedestrian is close to the crosswalk
(refer to Table 1) and not when he or she is walking along
the sidewalk to reach the crosswalk (refer to paths 3 and 6
in Figure 3A).
Current IVE-based studies are also limited by the range
of the possible pedestrian motions (Deb et al., 2017; Pillai,
2017). This limits the number of potential scenarios that can
be explored. Our experimental setup with an omnidirectional
treadmill provided unlimited range to the pedestrians to walk
in the IVE. This allowed us to have a crossing task where
participants had an approach distance to the crosswalk in
addition to the actual crossing, which is more comparable to
real-life crossing situations. Furthermore, the extended range
allowed us to examine pedestrian–AV interactions in more
complex scenarios with wider roads. Our IVE setup also
facilitated study of pedestrian gaze behavior. Our methodology
to automatically identify AOI in real time was a precursor
to developing algorithms that can identify real-time AV trust
through gaze.
LIMITATIONS
This study has several limitations. First, the AV behavior
models were based only on discrete states driven by the
pedestrian’s position and did not incorporate continuous
dynamics. Moreover, pedestrian intent was derived from
only their position and did not consider their orientation.
Second, we conducted our study in an IVE, which is a
controlled experimental setting. Owing to the presence of other
environmental and situational factors, participants might react
differently in an actual crosswalk, resulting in different trusting
behaviors. However, some evidence suggests that this is not the
case (Heydarian et al., 2015; Deb et al., 2017). Specifically, Deb
et al. (2017) found that pedestrians’ reactions to traffic situations
in an IVE were similar to those in the real world. Nonetheless, we
acknowledge this as a potential limitation.
Third, our study only examined fully automated vehicles
without humans. It is unclear whether our findings can be
generalized to AVs with safety drivers or partially automated
vehicles. Fourth, our study considered only one kind of an
AV, a sedan. We acknowledge that there may be behavioral
differences due to the size and type of the AV (de Clercq et al.,
2019). Fifth, although we employed a gaze analysis methodology
that enables automatic AOI identification in real time, it does
Frontiers in Robotics and AI | www.frontiersin.org 14 November 2019 | Volume 6 | Article 117

Jayaraman et al. Trust in AV at Crosswalks
not segregate fixations from saccades. Fixations indicate the
steady gaze focused on a particular region, whereas saccades
represent rapid movements between the fixations. Future work
should consider incorporating fixations into the methodology.
Sixth, our study design involved only one human participant
on a unidirectional street. Future studies could include more
pedestrians, different road layouts, and bidirectional streets.
Finally, our participants were all young university students
who might have a similar attitude toward AVs. Future studies
might enlist representatives from the general population or
older individuals whose trust or acceptance of AVs could
vary significantly (Schoettle and Sivak, 2014; Hulse et al.,
2018).
CONCLUSION AND FUTURE WORK
We formally examined the effects of implicit AV communication
through driving behavior and traffic signal on pedestrian
trust in AVs. We examined the moderation effects of
traffic signal on the impact of AV driving behavior on
pedestrian trust in AVs. We also established the relationships
between trust and trusting behaviors. Pedestrians trusted
the AVs more when the AVs exhibited defensive driving
behavior. Furthermore, pedestrians trusted AVs more
at signalized crosswalks, and this trust was unaffected
by the driving behavior of the AVs. This study thus
revealed significant relationships among AV driving
behavior, crosswalk type, pedestrian’s trust in the AVs, and
trusting behavior.
Nonetheless, there is still much to learn. For example,
under low trust situations (such as aggressive AV driving
at unsignalized crosswalks), ways to promote trust
through other communication means could be explored.
Using an explicit communication interface is one way
to provide AV intent information that can reduce the
uncertainty and promote AV trust. Furthermore, the
relative influence of the two sources of information—AV
explicit interface and traffic signal—on pedestrian trust can
be examined.
In addition, the correlation between trust and observable
trusting behavior opens many avenues for research. Trust is
not readily observable. However, by establishing a correlation
between trust and observable trusting behavior, trusting behavior
can be used as a proxy for measuring trust. This would
enable real-time assessment of trust under different conditions.
Further research on real-time trust measurement would enable
us to develop prediction models of pedestrian trust in
AVs based on various pedestrian, vehicle, situational, and
environmental factors. Future research can also aim to validate
the findings from this study in a real-world setting using
Wizard of Oz AVs.
DATA AVAILABILITY STATEMENT
All datasets generated for this study are included in the
article/Supplementary Material.
ETHICS STATEMENT
This study was carried out in accordance with
the recommendations of American Psychological
Association Code of Ethics and the Institutional Review
Board at the University of Michigan with written
informed consent from all subjects. All subjects gave
written informed consent in accordance with the
Declaration of Helsinki. The protocol was approved
by the Institutional Review Board at the University
of Michigan.
AUTHOR CONTRIBUTIONS
SJ, CC, DT, XY, AP, KT, and LR contributed to the conception
and design of the study. SJ and CC contributed to the experiment
execution. SJ and LR contributed to the analysis. SJ, DT, XY, AP,
KT, and LR wrote sections of the manuscript and contributed to
manuscript revision.
FUNDING
Toyota Research Institute (TRI) provided funds to assist the
authors with their research, but this article solely reflects the
opinions and conclusions of its authors and not TRI or any
other Toyota entity. The work was also supported in part by the
National Science Foundation.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/frobt.
2019.00117/full#supplementary-material
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Conflict of Interest: KT is an employee of TRI.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2019 Jayaraman, Creech, Tilbury, Yang, Pradhan, Tsui and Robert.
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Frontiers in Robotics and AI | www.frontiersin.org 17 November 2019 | Volume 6 | Article 117

Jayaraman et al. Trust in AV at Crosswalks
APPENDIX
1. LATIN SQUARE DESIGN
The standard Latin square design that we employed in the
study is given below, with the six treatment conditions:
defensive unsignalized (A), normal unsignalized (B), aggressive
unsignalized (C), defensive signalized (D), normal signalized (E),
and aggressive signalized (F). Each condition appears exactly
once in each row and once in each column, which resulted in
a set of six condition sequences. The set was designed such
that every treatment condition appears exactly once before and
once after every other condition. The set was repeated five
times to get 30 condition sequences for the 30 participants in
the study.
TABLE A1 | Latin square design employed in the study.
Order of treatment conditions
Subject number 1st 2nd 3rd 4th 5th 6th
1 A F B E C D
2 B A C F D E
3 C B D A E F
4 D C E B F A
5 E D F C A B
6 F E A D B C
2. POST-TREATMENT TRUST
QUESTIONNAIRE
The below questionnaire has been adapted from Muir (1987),
which examines trust in automation. Please indicate the extent
to which you believe the autonomy has each of the following
traits (from 1 representing “none at all” to 7 representing
“extremely high”).
•Competence: To what extent did the autonomous cars perform
their function properly i.e., recognizing you and reacting
for you?
•Predictability: To what extent were you able to predict the
behavior of the autonomous cars from moment to moment?
•Dependability: To what extent can you count on the
autonomous cars to do its job?
•Responsibility: To what extent the autonomous cars seemed to
be wary of their surroundings?
•Reliability over time: To what extent do you think the
autonomous car’s actions were consistent through out
the interaction?
•Faith: What degree of faith do you have that the autonomous
cars will be able to cope with all uncertain ties in the future?
3. SIMULATOR SICKNESS
QUESTIONNAIRE (SSQ)
Please indicate how much each symptom below is affecting you
right now [survey from Kennedy et al. (1993)].
TABLE A2 | Simulator sickness questionnaire.
Sensation 0 1 2 3
General Discomfort None Slight Moderate Severe
Fatigue None Slight Moderate Severe
Headache None Slight Moderate Severe
Eye Strain None Slight Moderate Severe
Difficulty focusing None Slight Moderate Severe
Increased salivation None Slight Moderate Severe
Sweating None Slight Moderate Severe
Nausea None Slight Moderate Severe
Difficulty concentrating None Slight Moderate Severe
Fullness of head None Slight Moderate Severe
Blurred vision None Slight Moderate Severe
Dizzy (eyes open) None Slight Moderate Severe
Dizzy (eyes closed) None Slight Moderate Severe
Vertigo None Slight Moderate Severe
Stomach awareness None Slight Moderate Severe
Burping None Slight Moderate Severe
Frontiers in Robotics and AI | www.frontiersin.org 18 November 2019 | Volume 6 | Article 117