# Building Trust Profiles in Conditionally Automated Driving

Lilit Avetisyan, Jackie Ayoub, X. Jessie Yang, Feng Zhou, _Senior Member,
IEEE_ Lilit Avetisyan and Jackie Ayoub contributed equally.J. Ayoub , L.
Avetisyan, and F. Zhou are with the Department of Industrial and
Manufacturing, Systems Engineering, The University of Michigan, Dearborn, 4901
Evergreen Rd. Dearborn, MI 48128 USA (e-mail: {jyayoub, lilita,
fezhou}@umich.edu).X. J. Yang is with Industrial and Operations Engineering,
University of Michigan, Ann Arbor, 500 S State St, Ann Arbor, MI 48109 USA
(e-mail: xijyang@umich.edu).Manuscript received Nov 17, 2022; revised xxx 26,
2022.

###### Abstract

Trust is crucial for ensuring the safety, security, and widespread adoption of
automated vehicles (AVs), and if trust is lacking, drivers and the public may
not be willing to use them. This research seeks to investigate trust profiles
in order to create personalized experiences for drivers in AVs. This technique
helps in better understanding drivers’ dynamic trust from a persona’s
perspective. The study was conducted in a driving simulator where participants
were requested to take over control from automated driving in three conditions
that included a control condition, a false alarm condition, and a miss
condition with eight takeover requests (TORs) in different scenarios. Drivers’
dispositional trust, initial learned trust, dynamic trust, personality, and
emotions were measured. We identified three trust profiles (i.e., _believers_
, _oscillators_ , and _disbelievers_) using a K-means clustering model. In
order to validate this model, we built a multinomial logistic regression model
based on SHAP explainer that selected the most important features to predict
the trust profiles with an F1-score of 0.90 and accuracy of 0.89. We also
discussed how different individual factors influenced trust profiles which
helped us understand trust dynamics better from a persona’s perspective. Our
findings have important implications for designing a personalized in-vehicle
trust monitoring and calibrating system to adjust drivers’ trust levels in
order to improve safety and experience in automated driving.

###### Index Terms:

Trust profiles, emotion, personality traits automated vehicles.

##  I Introduction

Automated vehicles (AVs) possess the potential to revolutionize the
transportation sector by offering safer and more efficient modes of
transportation [1]. Nevertheless, the widespread acceptance and implementation
of AVs heavily rely on users’ trust in this technology. In the absence of
trust, drivers may exhibit reluctance towards adopting AVs, thereby impeding
or even hindering the deployment of this transformative technology.
Consequently, it becomes imperative to design AVs in a manner that cultivates
an optimal level of trust, encouraging users to accept and embrace them.

Thus, many researchers have conducted studies on trust in automated vehicles
(AVs) and other types of automation, with a particular focus on factors that
contribute to and influence trust, such as system transparency, reliability,
and performance[2, 3]. For example, both Ayoub et al. [2] and Azevedo-Sa et
al. [4] demonstrated that when an AV exhibited a high level of system
reliability and performance, thereby operating as expected without errors,
participants were more inclined to trust the vehicle. Additionally, several
studies (e.g., [3, 5, 6]) highlighted the importance of enhancing system
transparency. By providing information about the system’s status and
explanations for automation failures, trust in automation and AVs was
increased.

The aforementioned studies have provided valuable insights to aid in the
design of automated systems. However, what appears to be lacking and
inconsistent is the consideration of the impact of individual differences on
trust in AVs, including factors such as age, driving experience, knowledge of
automation systems, personality traits, and emotions (e.g., [7, 8, 9]), among
others. For instance, empirical studies (e.g., [10]) indicated that older
adults exhibit a higher tendency to overtrust automated systems compared to
younger age groups. Conversely, survey findings revealed that older
participants (60 years and above: 45.2%) expressed greater concerns and,
therefore, exhibited lower levels of trust in AVs compared to their younger
counterparts (18-29 years old: 26.1%) [11]. It is important to note that
individuals of different age groups often employ distinct strategies
influenced by various factors such as knowledge, personality traits, and
driving experience. Moreover, the specific impact of age may vary in different
contexts [12].

Moreover, the establishment of trust in AVs encompasses both cognitive and
emotional factors, with emotions serving as the primary determinant of
trusting behavior [13]. However, the influence of emotions on trust in AVs has
received limited attention from researchers. Although some efforts have been
made to explore the role of emotions in trust towards AVs, further
investigation is necessary to fully comprehend the depth of this relationship
[9, 14]. Specifically, additional research is required to gain insight into
the intricate nuances of how various emotions impact trust in AVs and how
these emotional responses may interact with other factors, such as the
reliability of the system, driver personality traits, and prior experiences
with AVs.

Another research gap in many previous studies [15, 16] pertains to the
investigation of individual differences, such as dispositional trust and
personality, in relation to trust dynamics. However, these studies primarily
focused on capturing trust levels before and after experiments or through
cross-sectional surveys, providing only a snapshot of trust dynamics. To gain
a comprehensive understanding, it is essential to explore how individual
factors influence trust dynamics across various profiles. Previous research
demonstrated the existence of different trust dynamics, including
_oscillators_ , _disbelievers_ , and Bayesian decision-makers, in the context
of human-robot interaction [17]. Therefore, it is imperative to examine the
associations between different individual factors and trust dynamics in the
domain of human-AV interaction.

Thus, in this study, we explored trust profiles by aggregating various types
of individual factors, including dispositional trust, initial learned trust,
personality traits, trust-related emotions, and dynamic trust elicited during
the experiment. These factors were examined across three levels of system
reliability, which were influenced by different types of errors.

First, we employed a user segmentation approach in our user research to assist
designers in understanding the behaviors of different trust profiles. This
approach is particularly valuable for comprehending user trust in AVs as it
enables designers to identify specific factors, particularly those related to
individual differences, that influence trust and subsequently design
interventions targeting these factors.

Second, building upon previous studies that demonstrated how dispositional
trust and initial learned trust encompass numerous individual factors such as
age, gender, culture, driving experience, and knowledge in automation [12], we
minimized the number of factors by investigating dispositional and initial
learned trust.

Third, we utilized a data-driven methodology that employed machine learning
models. This included using K-means clustering to identify trust profiles,
multinomial logistic regression with SHAP (SHapley Additive exPlanations) to
validate these profiles [18, 19], and conducting statistical comparisons among
the identified trust profiles. By gaining insights into the trust factors
associated with different profiles, designers are empowered to develop trust-
oriented solutions that effectively address these factors.

Overall, this research contributes to the understanding of trust dynamics in
AVs and provides practical guidance for designers to enhance trust through
tailored design interventions.

##  II Related Work

In order to understand trust in AVs, it is important to understand the factors
that affect trust. Researchers have identified a wide range of factors that
impact trust in AVs. Hoff and Bashir (2015) [12] proposed a taxonomy of these
factors based on three types of trust, namely dispositional, learned, and
situational trust. In the context of driver-AV interaction, dispositional
trust represented drivers’ tendency to trust AVs, including typical factors
such as age, gender, culture, and personality; learned trust represents the
drivers’ assessment of the AV based on their past experience (e.g.,
preexisting knowledge about the AV) or current interaction (e.g., reliability
and performance of the AV) with the AV; situational trust is dependent on the
interaction between drivers and the automation in specific contexts, including
external environment of the interaction between the driver and the AV (e.g.,
the complexity and risks associated with the task) and internal
characteristics of the driver (e.g., the emotional states and cognitive
workload of the driver).

It is crucial to investigate individual factors that influence dispositional
trust. For instance, Robert et al. [20] found that participants in high-
context cultures, such as East Asia (including China, South Korea, Japan)
exhibited greater trust in AVs compared to individuals from low-context
cultures (e.g., Western Europe and US) when explanations were provided.
Personality was also identified to be a vital factor on trust. Chien et al.
[21] demonstrated that higher levels of agreeableness and conscientiousness in
personality traits were associated with increased initial trust in automation,
with agreeableness and conscientiousness being two of the dimensions in the
Big Five model of personality. However, experimental examination of these
factors can often be challenging due to contextualization and potential
interactions with other factors, such as age and driving experience. This can
result in inconsistent findings, as observed in the effects of age on trust in
AVs [10, 11]. Consequently, it is crucial to investigate multiple individual
factors concurrently to gain a comprehensive understanding of their overall
impact on trust in AVs.

For learned trust, numerous studies examined preexisting knowledge and
experience of AVs through surveys and current interaction through experimental
studies. For example, Ayoub et al. [8] found that knowledge of AVs generally
increased trust in AVs while experience in driving decreased trust in AVs. As
mentioned previously, during interaction with AV, a high level of system
transparency, reliability, and performance generally increased trust in AVs
[2, 3, 4]. However, it is crucial to consider the temporal aspect of trust
development, particularly during extended interaction periods with AVs. To
explore this, Bhat et al. [17] conducted a study involving sequential
decision-making tasks and employed a clustering model to identify distinct
trust dynamics and different types of trust profiles.

For situational trust, both external variability and internal factors should
be considered to understand trust in AVs. For example, Azevedo-Sa et al. [4]
found that trust in AVs increased over time when internal risk was low (i.e.,
system reliability was high) while external risks (visibility in driving) did
not impact trust significantly.

While the effects of many other factors on trust were investigated, less is
known about the effects of emotions on trust in AVs. Previous research has
suggested that positive emotions may foster trust due to their association
with feelings of safety and security [22]. For instance, Du et al. [23]
discovered that positive emotions improved takeover performance in AVs,
subsequently leading to increased trust. Ayoub et al. [8] demonstrated a
positive correlation between the feeling of excitement and participants’ trust
in AVs. Conversely, negative emotions have been found to diminish trust, even
when unrelated to trust-related decisions [24]. Myers et al. [22] investigated
the influence of three negative emotions (anger, guilt, and anxiety) on trust
and found that negative emotions with low certainty appraisals (e.g., anxiety)
reduced trust, whereas those with high certainty (e.g., anger and guilt) had
no discernible effects on trust. Additionally, Ayoub et al. [1] revealed that
negative emotions such as concerns and worries decreased parents’ trust in
automated school buses. Thus, it is imperative to examine the effects of
emotions and their role in identifying trust profiles in the context of AVs.

##  III Methodology

Fig. 1 provides an overview of the methodology in this study and the details
are described in the following.

###  III-A Participants

In this study, we recruited 74 university students, each of whom received a
compensation of $25 for their participation, involving a duration of
approximately one hour. Four participants were excluded from further analysis
due to missing data. Data from the remaining 70 participants (mean age = 21.3,
SD = 3.0; age range: 18 to 33; 32 female and 38 male participants) were
utilized for subsequent analysis. All participants satisfied the requisite
criterion of holding a valid driver’s license with normal or corrected-to-
normal vision.

Figure 1: Framework of the proposed study. Figure 2: Experiment setup.

###  III-B Apparatus and stimuli

The study employed a desktop-based driving simulator from Realtime
Technologies Inc. (RTI, MI, USA) for data collection, as illustrated in Fig.
2. The simulation setup comprised three LCD monitors, integrated with a
Logitech driving kit, and two touchscreens (i.e., a tablet and phone), located
on the participant’s right side, for the non-driving related task (NDRT) and
trust rating entry. The NDRT involved a custom-designed Tetris game,
implemented using the PyGame library in Python. Participants were required to
drag tiles to navigate the game, which they could pause to respond to takeover
requests (TORs), and then resume from the same position afterward. Trust was
evaluated using a questionnaire created with Qualtrics (Provo, UT,
www.qualtrics.com) on a mobile phone. Participants were prompted to rate their
trust levels on a scale ranging from 0 to 10, using a single-item question
(“How much do you trust the AV?”) every 25 seconds, following previous studies
[25, 26].

The driving simulation was designed to simulate SAE Level 3 automation. To
activate the automated mode, participants were required to depress a red
button situated on the steering wheel. Upon initiation of the automated mode,
an auditory message, “Automated mode engaged,” would be emitted. In the event
of a TOR, participants would be alerted by an auditory warning of “Takeover”
and the automated mode would be promptly disengaged to facilitate the
participant’s control of the vehicle. In instances where participants failed
to resume control of the vehicle within the designated time frame, an auditory
emergency stop warning (“Emergency Stop”) would be activated to avert any
potential collisions.

###  III-C Experimental design

In this study, we employed a between-subjects design in which participants
were assigned to one of the three conditions: a control condition where all
eight TORs were valid, a false alarm condition that consisted of four valid
TORs and four TORs with false alarms (the 2nd, 3rd, 5th, and 6th), and a miss
condition where four of the eight TORs were valid, while the AV missed the
other four TORs (the 2nd, 3rd, 5th, and 6th). This approach was adopted to
evaluate the impact of different levels of automation performance on trust, as
previous studies indicated that both false alarms and misses reduced trust in
automated systems [27].

During the simulation, standard roadway features were used to signify the
occurrence of a TOR event, such as the presence of deer, bicyclists,
pedestrians, construction zones, vehicle sudden stops, buses with sudden
stops, and police vehicles on the shoulder (see Figures 3). The TOR events
were equally distributed between rural and urban areas, with four TOR events
taking place in each location. The order of the rural and urban scenarios was
alternated to minimize potential order effects.

###  III-D Experimental procedure

Upon arrival, participants were asked to complete a consent form and an online
demographic survey. After that, participants received an introduction and
watched a short video about the tasks they were required to do. Then,
participants completed an online survey that consisted of personality
information, propensity to trust AVs, and initial learned trust. The Big Five
Model was used to examine different personality traits [28] of the
participants using 5-point Likert scales (see Table I). The dispositional
trust was measured using a six-item trust scale proposed by Merritt et al.
[29]. The statements (see Table III) were rated with 7-point Likert scales.
The initial learned trust was assessed with ten items (see Table III) using
7-point Likert scales. Subsequently, the participants underwent a training
session to familiarize themselves with the driving simulator and experimental
protocol. They were required to assess the automated mode functionality of the
vehicle and instructed to maintain vigilance and assume control when
necessary. Furthermore, they were informed about potential system failure
scenarios, including instances of failing to detect obstacles (in the miss
condition) and false alarms of TORs (in the FA condition). There were two
drives, including urban and suburban with approximately 15 minutes each. The
entire experiment was completed within a duration of approximately 60 minutes.
To monitor their trust levels dynamically, participants were prompted to
respond to a single-item trust rating, ranging from 0 to 10, every 25 second
[30, 26], with the aim of avoiding any excessive mental or emotional stress
[31]. Additionally, they were asked to rate their trust levels after
completing the driving simulation [25]. Finally, participants rated their
anticipated emotional responses to AVs using a 7-point Likert scale that
consisted of 19 emotion items, including disdainful, scornful, contemptuous,
hostile, resentful, ashamed, humiliated, confident, secure, grateful, happy,
respectful, nervous, anxious, confused, afraid, freaked out, lonely, and
isolated. These emotions have previously been used to investigate trust in
human-machine automation [32, 9].

Figure 3: Examples of takeover event in (a) a suburban area with dears ahead
(b) an urban area with a bus sudden stop ahead.

##  IV Results

###  IV-A Identifying trust profiles

We attempted to identify trust profiles using a clustering model based on
various measures recorded during the experiment, taking into account factors
such as personality, emotions, dispositional, initial learned, and real-time
dynamic trust, as well as the performance of the AV (i.e., control, false
alarm and misses). To do this, 48 features from these measures were first
normalized between 0 and 1 and then used as input to the K-means clustering
model, which was performed in the Azure Machine Learning Studio environment,
with the number of cluster chosen as 3 to be the optimal number. As a result,
three clusters were formed and named as follows: 1) _oscillators_ , consisting
of 23 participants, 2) _believers_ , consisting of 31 participants, and 3)
_disbelievers_ , consisting of 16 participants.

Examining the trust profiles, we found that first profile (i.e.,
_oscillators_) corresponded to drivers who exhibited a moderate level of
dynamic trust (Mean = 0.61 and SD = 0.23), but reported high levels of
dispositional (Mean = 0.67 and SD = 0.19) and initial learned trust (Mean =
0.65 and SD = 0.22). The individual observations showed that participants’
dynamic trust varied from high to low and vice versa during the experiment.
Therefore, this trust profile was referred to as the _oscillators_. Despite
the fact that _oscillators_ reported the highest levels of dispositional and
initial learned trust, their trust level were dynamically evolving based on
their most recent experiences resulting in rapid oscillation (see Fig. 4).

The second trust profile, referred to as _believers_ , consisted of drivers
who showed a relatively high level of dynamic trust (Mean = 0.69 and SD =
0.15) during the experiment, but reported moderate levels of dispositional
(Mean = 0.53 and SD = 0.22) and initial learned trust (Mean = 0.55 and SD =
0.21). In this profile, the dynamic trust showed an increasing trend during
the experiment as participants rated their trust considering their previous
knowledge and experience besides the most recent ones happening during the
experiment

The last profile (i.e., _disbelievers_) included drivers who exhibited the
lowest levels of dynamic trust (Mean = 0.45 and SD = 0.26), as well as low
levels of dispositional (Mean = 0.40 and SD = 0.24) and initial learned trust
(Mean = 0.42 and SD = 0.24). The individual data observations of
_disbelievers_ showed that their levels of dynamic trust either decreased or
remained low throughout the experiment. Also, drivers in this profile tended
to be carping rather than being optimistic about the situation and the AV’s
overall performance. Therefore, this trust profile was referred to as
_disbelievers_.

Figure 4: Dynamic trust evaluation of three types of trust profiles during the
experiment where the x-axis is the ordinal number of trust ratings in the
experiment. Note the number of points of each participant were different and
we aggregated them by the order of their self-reported trust to show the
dynamics of trust of three profiles.

###  IV-B Personality, Emotions, Dispositional and Initial Learned Trust

Personality: We evaluated the personality of the participants using the Big
Five Inventory scale (BFI-10) [33], which measured character traits in five
dimensions as shown in Table I: Extraversion (i.e, sociable and reversing
reserved), Agreeableness (i.e, trusting and reversing carper),
Conscientiousness (i.e, meticulous and reversing lazy), Neuroticism (i.e,
nervous and reversing relaxed), and Openness to Experience (i.e, imaginative
and reversing artistic). We conducted a one-way ANOVA test to compare these
dimensions across the trust profiles and found a significant difference in
Agreeableness $(F(2,67)=2.452,p=0.033)$. The post-hoc analysis with Tukey-
Kramer test showed that _oscillators_ had significantly higher levels of
Agreeableness compared to _disbelievers_ (see Fig. 5). In particular, a
significant difference was found in the item “I see myself as someone who
tends to find fault with others” statement $(p=0.015)$ where _disbelievers_
had higher ratings than _believers_ and _oscillators_ (see Table I). However,
there were no significant differences in the other four personality
characteristics (i.e., Extraversion, Conscientiousness, Neuroticism, and
Openness to Experience). This result was consistent with the SHAP model
outcome, as only the “carper” feature, which represented the above mentioned
question in the personality feature set, was selected as an important feature
in the model.

Figure 5: Mean and standard deviation (SD) of Big Five factors (dimensions) of
personalities among trust profiles. TABLE I: Mean, standard deviation (SD) and
$p$-value of individual items in personality. Note: The profiles that share
the same letter (i.e., a, b or c) are not statistically different.

Feature | _oscillators_ | _believers_ | _disbelievers_ |  $p$-value
---|---|---|---|---
Reserved | 0.51 (0.27) | 0.49 (0.36) | 0.65 (0.31) | 0.085
Trusting | 0.77 (0.21) | 0.65 (0.32) | 0.67 (0.34) | 0.305
Lazy | 0.46 (0.28) | 0.53 (0.30) | 0.47 (0.34) | 0.622
Relaxed | 0.66 (0.21) | 0.60 (0.29) | 0.53 (0.34) | 0.344
Artistic | 0.51 (0.30) | 0.52 (0.33) | 0.48 (0.37) | 0.950
Sociable | 0.74 (0.22) | 0.75 (0.25) | 0.58 (0.36) | 0.101
Carper *** | 0.39 (0.28) a | 0.38 (0.28) ab | 0.62 (0.25) c | 0.000
Meticulous | 0.87 (0.15) | 0.78 (0.25) | 0.89 (0.13) | 0.127
Nervous | 0.57 (0.26) | 0.57 (0.27) | 0.56 (0.28) | 0.991
Imaginative | 0.76 (0.19) | 0.82 (0.22) | 0.72 (0.27) | 0.291

Emotions: The results of the one-way ANOVA test indicated that there were
significant differences in all the emotions among the three trust profiles,
except “Grateful”, as detailed in Table II. To understand the underlying
structure of emotions associated with each trust profile, we conducted an
exploratory factor analysis (EFA) and identified three subsets that combined
the correlated emotions, i.e resentfully aversion: disdainful, scornful,
contemptuous, hostile, resentful, ashamed, humiliated, happily acceptance:
confident, grateful, secure, happy, respectful, not lonely, not isolated, and
nervously fear: nervous, anxious, confused, afraid, freaked out. The results
showed that _believers_ had significantly lower scores for emotions related to
resentfully aversion compared to the other trust profiles ($p=0.000$). They
also had significantly higher scores for confidence and security in the
happily acceptance category compared to _oscillators_ , and higher scores for
happiness, gratitude, and respect compared to _disbelievers_. In the nervously
fear category, _believers_ showed significantly lower scores than the other
trust profiles ($p=0.000$). Moreover, participants in the _oscillator_ profile
had significantly higher scores for happiness ($p=0.016$) compared to
_disbelievers_ (see Fig. 6).

Figure 6: Mean and standard deviation (SD) of three emotion categories among
trust profiles. TABLE II: Mean, standard deviation (SD) and $p$-value of
emotions. Note: The profiles that share the same letter (i.e., a, b or c) are
not statistically different.

Feature | _oscillators_ | _believers_ | _disbelievers_ |  $p$-value
---|---|---|---|---
Lonely | 0.18 (0.22) a | 0.06 (0.17) ab | 0.44 (0.35) c | 0.000
Isolated | 0.20 (0.22) a | 0.08 (0.17) ab | 0.44 (0.35) c | 0.000
Resentful | 0.30 (0.27) a | 0.10 (0.20) b | 0.34 (0.31) ac | 0.003
Humiliated | 0.30 (0.30) a | 0.06 (0.12) b | 0.36 (0.40) ac | 0.000
Ashamed | 0.33 (0.32) a | 0.06 (0.13) b | 0.39 (0.39) ac | 0.000
Hostile | 0.37 (0.27) a | 0.10 (0.25) b | 0.56 (0.38) ac | 0.000
Disdainful | 0.41 (0.22) a | 0.09 (0.18) b | 0.39 (0.28) ac | 0.000
Afraid | 0.43 (0.32) a | 0.20 (0.24) b | 0.50 (0.23) ac | 0.000
Scornful | 0.45 (0.35) a | 0.13 (0.29) b | 0.38 (0.32) ac | 0.001
Freaked out | 0.45 (0.32) a | 0.06 (0.14) b | 0.40 (0.22) ac | 0.000
Grateful | 0.54 (0.23) | 0.62 (0.21) | 0.48 (0.20) | 0.080
Nervous | 0.54 (0.25) a | 0.33 (0.26) b | 0.64 (0.20) ac | 0.000
Confused | 0.56 (0.24) a | 0.24 (0.26) b | 0.68 (0.18) ac | 0.000
Contemptuous | 0.57 (0.35) a | 0.23 (0.33) b | 0.52 (0.27) ac | 0.001
Secure | 0.57 (0.23) a | 0.75 (0.14) b | 0.46 (0.25) ac | 0.000
Confident | 0.59 (0.25) a | 0.72 (0.12) b | 0.55 (0.15) ac | 0.003
Happy | 0.59 (0.27) a | 0.63 (0.20) ab | 0.34 (0.20) c | 0.000
Respectful | 0.61 (0.27) a | 0.72 (0.21) ab | 0.45 (0.19) ac | 0.001
Anxious | 0.66 (0.27) a | 0.38 (0.27) b | 0.70 (0.24) ac | 0.000

Dispositional, Initial Learned, and Dynamic Trust: With regard to
dispositional and learned trust, the results of one-way ANOVA showed that
there was a significant difference between three trust profiles (see Table
III) for all the factors measuring dispositional and initial learned trusts
($p<0.05$), except one from initial learned trust that measured trust in
delegating control task to AV when driving was boring ($p=0.08$). As for
dynamic trust, the results showed that _disbelievers_ had significantly
different trust levels in the urban and suburban areas. Specifically, in the
urban area, _disbelievers_ had a lower trust level compared to the other trust
profiles ($p=0.000$), while in the suburban areas, the difference was
significant compared to _believers_ ($p=0.017$).

TABLE III: Mean, standard deviation (SD) and $p$-value of dispositional and
initial learned trust. Note: The profiles that share the same letter (i.e., a,
b or c) are not statistically different.

| Feature | _oscillators_ | _believers_ | _disbelievers_ |  $p$-value
---|---|---|---|---|---
Dispositional | I usually trust self-driving vehicles until there is a reason not to | 0.81 (0.16) a | 0.67 (0.20) b | 0.45 (0.26) c | 0.000
For the most part, I distrust self-driving vehicles | 0.24 (0.21) a | 0.39 (0.21) b | 0.57 (0.25) c | 0.000
In general, I would rely on a self-driving vehicle to assist me |  0.78 (0.18) a | 0.61 (0.19) b | 0.41 (0.27) c | 0.000
My tendency to trust self-driving vehicles is high |  0.78 (0.19) a | 0.53 (0.21) b | 0.35 (0.24) c | 0.000
It is easy for me to trust self-driving vehicles to do their job | 0.78 (0.16) a | 0.56 (0.21) b | 0.42 (0.25) bc | 0.000
I am likely to trust self-driving vehicles even when I have little knowledge about it | 0.66 (0.27) a | 0.42 (0.28) b | 0.23 (0.19) bc | 0.000
Initial learned | I would feel safe in a self-driving vehicle | 0.80 (0.11) a | 0.65 (0.16) b | 0.46 (0.22) c | 0.000
The self-driving vehicle system provides me with more safety | 0.70 (0.25) a | 0.45 (0.20) b | 0.39 (0.22) bc | 0.000
I would rather keep manual control of my vehicle than delegate it to the self-driving vehicle system on every occasion | 0.39 (0.30) a | 0.57 (0.29) ab | 0.76 (0.18) bc | 0.000
I would trust the self-driving vehicle system decisions | 0.76 (0.14) a | 0.65 (0.13) ab | 0.43 (0.25) c | 0.000
I would trust the self-driving vehicle system capacities to manage complex driving situations | 0.64 (0.24) a | 0.42 (0.21) b | 0.33 (0.28) bc | 0.000
If the weather conditions were bad, I would delegate the driving task to the self-driving vehicle system | 0.45 (0.30) a | 0.35 (0.25) ab | 0.22 (0.19) bc | 0.030
Rather than monitoring the driving environment, I could focus on other activities confidently | 0.61 (0.24) a | 0.48 (0.29) ab | 0.26 (0.24) c | 0.001
If driving was boring for me, I would rather delegate it to the self-driving vehicle system than do it myself | 0.70 (0.27) | 0.67 (0.21) | 0.52 (0.28) | 0.081
I would delegate the driving to the self-driving vehicle system if I was tired | 0.79 (0.24) a | 0.74 (0.20) ab | 0.59 (0.29) bc | 0.042
I would trust the self-driving vehicle | 0.76 (0.15) a | 0.55 (0.20) b | 0.30 (0.26) c | 0.000

###  IV-C Validating Trust Profile Models

In order to validate the trust profile models, we used the cluster membership
as the ground truth and tested the accuracy of the clustering model by
training supervised machine learning models with all the 48 features. We used
an Azure automated ML experiment job to identify the best machine learning
model in Azure automated ML experiment by fine-tuning the hyperparameters. The
multinomial logistic regression model was found to perform the best across a
large number of models, including XGBoost, LightGBM, Random Forest, etc. The
results showed that the model was able to predict the trust profile with
F1-score = 0.90.

We also used SHapley Additive exPlanations (SHAP) explainer to interpret the
model and understand the importance of individual features in the model’s
decision-making process. Fig. 7 illustrates the feature importance rankings,
from the most influential to the least influential on the prediction. The
features of condition (miss, false alarm, or control), emotions of freaked out
and confused were found to be the most significant ones in predicting the
trust profiles, regardless of whether they had a positive or negative effect.
The color coding indicates the feature importance in distinguishing between
different trust profiles. We used a feature selection method to improve the
model’s performance while preserving the patterns and relationships in the
data, as suggested by Ayoub et al. [18] and [19]. We added one feature at a
time based on the feature importance ranking from the SHAP explainer and
validated the results with stratified 5-fold cross validation. As shown in
Fig. 8, the model reached the highest performance (F1-score = 0.90) using the
top 25 features, i.e. condition, emotions: freaked out, lonely, confused,
disdainful, contemptuous, isolated, hostile, ashamed, anxious, scornful,
nervous, humiliated, happy, personality: carper, initial learned trust: I
could confidently focus on NDRT, I would keep manual control, I would consider
AV more safe than manuals, I would trust in complex situations, I would trust
AV, dispositional trust: I likely to trust AV without knowledge, I have high
tendency to trust AV, I easily can trust AV, I would rely on an AV, I mostly
distrust AV). Overall, across all the 48 features, the F1-score ranged from
0.52 to 0.90, and the accuracy ranged from 0.60 to 0.90.

Even though the experiment condition was found to be the most influential
feature in predicting these three trust profiles, it was not sufficient to
determine the identified trust profiles alone. As shown in Fig. 9, there was
no one-to-one mapping relationships between the participants in three
conditions, including control, FA, and misses and those formed the three
identified trust profiles. Also shown in Fig. 8, the condition alone only had
F1-score = 0.52 and accuracy = 0.60 in predicting trust profiles. Other
factors, such as emotions, personality, initial learned trust, and
dispositional trust, also played important roles in determining these trust
profiles.

Figure 7: Summary of SHAP values where the features are ordered from the
highest to the lowest effect on the prediction. In the figure, the prefix “p_”
in features indicates personality-related items, “d_” indicates dispositional
trust items, and “l_” indicates learned trust items. Figure 8: The impact of
individual features on overall performance of the model. During each
iteration, a single feature was added to the model based on SHAP importance
ranking. Figure 9: The distribution of the clusters across the three
conditions.

##  V Discussion

###  V-A Identifying Trust Profiles

In this study, we aimed to identify trust profiles of drivers in conditionally
automated driving using a multidimensional dataset that included drivers’
personality, dispositional trust, initial learned trust, dynamic trust levels,
and emotions. First, we performed a clustering method to group the drivers
exhibiting similar behaviors and unveil the relationships between the
investigated features. We identified three types of trust profiles, namely
_oscillators_ , _believers_ , and _disbelievers_. Then, we used a multinomial
logistic regression model to validate the trust profile clustering model and
the results showed that the model could predict the driver trust profile with
an F1-score of 0.90. The important features selected by SHAP explainer
indicated that the formation of dynamic trust in AV was highly associated with
system performance (e.g., false alarms, misses) emotions, initial learned
trust, dispositional trust, and personality, etc. The three conditions in the
experiment did not result in three distinct trust profiles. Instead other
factors also played important roles.

The trust profile of _oscillators_ was characterized by a moderate level of
dynamic trust, which fluctuated based on the recent experiences. This profile
corresponded to drivers who were generally optimistic about the AV’s
performance and in some situations were willing to take risks instead of
taking over the vehicle. Nevertheless, the emotional state of _oscillators_
was significantly impacted when the AV’s performance included errors,
indicating that the trust oscillation had an impact on the intensity of
particular emotions [9].

The trust profile of _believers_ was characterized by a relatively high level
of dynamic trust, which increased over time during the experiment. This
profile corresponded to drivers who were confident in AV’s abilities to
control the vehicle and were willing to delegate the driving task to the
system. From the emotion point of view, _believers_ had a more positive
attitude towards AV performance and were less prone to fear or disappointment
when encountering failures. Specifically, they felt a greater sense of safety
and were less vulnerable to false alarms, even if the takeover requests were
unfounded, in contrast to _disbelievers_ who regarded such incidents as severe
system malfunctions.

The _disbelievers_ , who were characterized by the lowest level of dynamic
trust, tended to be pessimistic and displayed a low level of trust even when
the AV performed properly. In addition to trust reports, the significantly
high ratings of nervousness and fear (see Table II), as well as for the manual
control and monitoring attributes in the learned trust (see Table III) during
the experiment verified that _disbelievers_ were not willing to delegate the
driving task to the vehicle or be engaged in NDRTs. This perspective might be
due to the limited understanding of the underlying processes of AV systems or
prior negative experiences with automated systems.

Regarding personality differences, the trust profiles showed similarities
except for the trait of agreeableness towards the system. _Disbelievers_ had a
significant disagreement with the system and were confident that they could
perform better than the AV, despite the AV performance.

The identified trust profiles exhibited patterns with the trust bases outlined
by previous studies stating that trust was always based on at least one of
three characteristics of an automation system, i.e. purpose, performance, and
process [34, 13]. Specifically, those who were categorized as _believers_ ,
the purpose was the dominant characteristic for trust and if the automation
failed, the affected trust dimension was the performance-related trust [35].
While _disbelievers_ trusted the AV system based on the process, meaning that
they did not trust the AV until they understood the decision making process.
_Oscillators_ trusted the system based on its performance based on the AV’s
capabilities to perform specific tasks in certain situations, and the trust
change depended on their comprehension of how the situation impacted on AV’s
capabilities.

###  V-B Implications

The identification of three distinct trust profiles, namely _believers_ ,
_disbelievers_ , and _oscillators_ , has significant implications for the
design of AVs.

For _believers_ , who exhibit high levels of trust in AVs, the design should
aim to provide features that increase their confidence and comfort levels,
such as clear and intuitive interfaces, well-defined communication protocols,
and robust safety features that effectively communicate the system’s status
and capabilities. They also tend to relate to the purpose of trust [34, 13]
and believe that automation could help improve efficiency, reduce errors, and
enhance safety by delegating their tasks to machines and freeing up their own
cognitive and physical resources to focus on other tasks. This could be not
limited to automated driving but in various domains, such as healthcare,
aviation, and manufacturing.

However, believers exhibited significantly more positive emotions, which might
impact their tendency to take risks [36] and potentially lead to overtrust in
AVs. To address this issue, the design should incorporate clear and
transparent communication that explains the system’s capabilities and
limitations by clearly showing the limitations of the system [37], and include
educational materials to help them understand how the technology works.

_Disbelievers_ , who exhibit low levels of trust in AVs, present a unique
challenge in the design process for them to adopt such technologies. First,
the design should incorporate effective training and education to build trust
and confidence over time. More important, they tend to relate more to the
process of the automation [34, 13], which might help improve their trust
levels in automated driving through transparency and predictability with
continuous feedback. If the vehicle is designed so that they can easily
understand what the system is doing and why it is doing it through clear and
intuitive communication as feedback, this would be helpful [6]. For example,
by including explaining why and what will information with speech and
augmented reality during the takeover process, participants reported to be
easy to use and accept SAE Level 3 vehicles [5]. We should also consider
predictability of the system so that the system’s behavior aligns with user
expectations and how well it performs in different scenarios. For example,
when participants received explanations about the vehicle’s behavior ahead of
the time and their possible projection in the future, they had better
situation awareness of the driving scenarios and trust in automated driving
[6]. These two strategies can help drivers to understand the system’s behavior
and performance, which in turn can enhance their learned trust and confidence
in the system.

Finally, for _oscillators_ , whose trust in AVs fluctuates based on their most
recent experience, the design should aim to deliver a consistently positive
experience. This can be achieved by providing reliable and consistent
performance across a variety of situations, such as inclement weather, heavy
traffic, and challenging road conditions. In this aspect, adaptive automation
might help _oscillators_ to calibrate their trust by adapting the level of
automation based on the drivers’ current trust level in AV and the system’s
past performance [38]. For instance, if the driver is confident and happy in
the AV, the system can perform in higher levels of autonomy and require less
intervention from the driver. On the other hand, if the driver is less
confident in the AV resulted from previous negative experiences, the system
can provide feedback about system status and require drivers intervention in
order to increase the feelings of confidence, control, and safety. Therefore,
integrating the adaptive automation into the AV system, designers would help
_oscillators_ to calibrate their trust in the automation system, improving
overall system performance and safety [38, 39].

###  V-C Limitations

The present study has several limitations that require further investigation
in future research. Firstly, the study was conducted using a low-fidelity
experimental setup utilizing a desktop driving simulator. The sample size of
70 participants was relatively small and we only considered a limited number
of factors to identify trust profiles. Moreover, the study sample was composed
primarily of university students, which resulted in a homogeneous sample,
regarding age, education, driving experience, and knowledge about AVs. To
overcome these limitations, future studies should be conducted in higher
fidelity experimental settings and with a larger and more diverse sample size.
More external factors should be included, such as time constraints, perceived
risks, complexity of NDRTs, and situation awareness [12, 40, 6].

Second, the assessment of trust was predominantly based on takeover scenarios
in which participants’ dynamic trust in control, misses, and FA conditions was
measured. The study only covered a limited number of potential takeover
issues. Therefore, future research should explore trust in other scenarios,
such as continuous driving performance in object detection, different driving
styles, and route selection recommendations. Overcoming these limitations
would allow for a better generalization of the trust profiles to other human-
machine interaction scenarios (e.g., [17]).

Finally, we only included a limited number of factors that formed the trust
profiles. While these factors provided valuable insights into the dynamics of
trust, they may not fully capture the complexity and diversity of trust. It is
important to acknowledge that trust is a multifaceted construct influenced by
various individual and contextual factors, which might not be fully accounted
for in this study. Therefore, the generalizability of the study findings to
other contexts and populations might be limited and future studies should
include a wider range of trust indicators to enhance the external validity of
the results.

##  VI Conclusions

The purpose of our research was to examine the trust profiles in AVs and
determine the underlying behavioral patterns of drivers that could be useful
for designing profiles-based systems. To accomplish this, we collected
multidimensional data and clustered them into three trust profiles:
_believers_ , _oscillators_ , and _disbelievers_. We used these profiles to
build a logistic regression model that could predict the trust profile with
accuracy of 0.89 and F1-score of 0.90. Additionally, we used SHAP explainer to
identify the most significant factors in the dataset that influenced the
creation of trust profiles. Furthermore, we investigated the dynamic trust
patterns among these profiles, as well as the associated initial learned
trust, dispositional trust, emotional and personality characteristics, based
on which we discussed how to develop a system that can adjust AV’s behavior
based on the driver’s trust profiles, and eventually promote their acceptance
and adoption of such technologies.

##  VII Acknowledgement

This research was supported by National Science Foundation.

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|  Lilit Avetisyan received her B.E. degree in 2017 and MS degree in 2019 in
Information Security from the National Polytechnic University of Armenia. She
is currently pursuing her Ph.D. degree in Industrial and Systems Engineering
at the University of Michigan, Dearborn. Her main research interests include
human-computer interaction, explainable artificial intelligence and situation
awareness.
---|---
|  Jackie Ayoub received her B.E. degree in mechanical engineering from Notre
Dame University, Lebanon, in 2016 and her master degree in Industrial and
Systems Engineering from University of Michigan, Dearborn, in 2017. She
received her Ph.D. in Industrial and Systems Engineering in the University of
Michigan, Dearborn, in 2022. She is currently a data scientist and human
factor engineer at Honda Research Institute, Detroit. Her main research
interests include human-computer interaction, human factors and ergonomics,
and sentiment analysis.
---|---
|  X. Jessie Yang is an Assistant Professor in the Department of Industrial
and Operations Engineering, University of Michigan, Ann Arbor. She earned a
PhD in Mechanical and Aerospace Engineering (Human Factors) from Nanyang
Technological University, Singapore. Dr. Yang’s research include human-
autonomy interaction, human factors in high-risk industries and user
experience design.
---|---
|  Dr Feng Zhou received the Ph.D. degree in Human Factors Engineering from
Nanyang Technological University, Singapore, in 2011 and Ph.D. degree in
Mechanical Engineering from Gatech Tech in 2014. He was a Research Scientist
at MediaScience, Austin TX, from 2015 to 2017. He is currently an Assistant
Professor with the Department of Industrial and Manufacturing Systems
Engineering, University of Michigan, Dearborn. His main research interests
include human factors, human-computer interaction, engineering design, and
human-centered design.
---|---