# Real-time Trust Prediction in Conditionally Automated Driving Using
Physiological Measures

Jackie Ayoub, Lilit Avetisian, X. Jessie Yang, Feng Zhou J. Ayoub , L.
Avetisian, 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 calibration presents a main challenge during the interaction between
drivers and automated vehicles (AVs). In order to calibrate trust, it is
important to measure drivers’ trust in real time. One possible method is
through modeling its dynamic changes using machine learning models and
physiological measures. In this paper, we proposed a technique based on
machine learning models to predict drivers’ dynamic trust in conditional AVs
using physiological measurements in real time. We conducted the study 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, a miss condition with eight takeover requests (TORs) in
different scenarios. Drivers’ physiological measures were recorded during the
experiment, including galvanic skin response (GSR), heart rate (HR) indices,
and eye-tracking metrics. Using five machine learning models, we found that
eXtreme Gradient Boosting (XGBoost) performed the best and was able to predict
drivers’ trust in real time with an f1-score of 89.1%. Our findings provide
good implications on how to design an in-vehicle trust monitoring system to
calibrate drivers’ trust to facilitate interaction between the driver and the
AV in real time.

###### Index Terms:

Trust prediction, physiological measures, real time,machine learning,
automated vehicles.

##  I Introduction

TRUST is essential in shaping driver-AV (automated vehicle) interaction [1,
2]. On one hand, the public seems reluctant about using AVs due to a lack of
trust [3]. On the other hand, people have been spotted falling asleep behind
the wheel of Tesla. To better manage driver-AV interaction, it is important
for researchers to develop methods for real-time measures of a driver’s trust
in AVs.

A variety of techniques have been proposed to measure trust in real-time. The
majority of previous studies have used self-reported assessment to measure
trust (e.g., [2, 4]). Using such methods, participants are asked to rate their
trust on a scale [5, 6]. These methods are simple, quick, and easy to
implement with low costs [7]. However, implementing them in real world
applications is challenging, as it is not practical to interrupt drivers to
ask them to rate their trust repeatedly [8].

Behavioral measures of trust (e.g., reaction time and facial expressions) are
an alternative approach to measuring trust in real time. They are helpful in
assessing people’s trust by observing and recording participants’ behavioral
processes [9, 10]. For instance, Korber et al. [9] used explicit (e.g., in the
form of reliance) and implicit (e.g., in the form of eye tracking) behavioral
measures of trust. Although eye tracking measures were useful in this task,
reliance on the system was often specific to the task at hands and thus is
often not well generalized to other situations.

The third type of trust measures are physiological measures. They capture
biological responses (e.g., hormonal levels, muscle movements, and neural
activation) using electrocardiogram (ECG), electroencephalogram (EEG), eye
gaze tracking, and skin conductance responses (SCRs) [11, 12, 13] during the
human-machine interaction process. Although these techniques are correlated
with human trust and do not interrupt drivers’ tasks, there are generally no
straightforward mapping relationships between these signals and trust dynamics
in different contexts of human-machine interaction.

In this paper, we investigated how system malfunctions affected participants’
dynamic trust. In SAE Level 3 automated vehicles, the system was capable of
identifying hazardous situations to trigger driver intervention. Two different
malfunctions, i.e., misses and false alarms [14], were designed to elicit
different levels of trust. Misses occurred when the system was not able to
identify potential hazards. On the other hand, a false alarm occurred when the
system identified non existing hazards. These different errors proved to have
different impacts on trust in previous studies (e.g., [15]).

In summary, the contributions of this study are as follows:

  * •

We used physiological measures to assess participants’ trust in real-time in
conditionally automated driving under two different system malfunctions (i.e.,
misses and false alarms).

  * •

We built different machine learning models to predict trust dynamics in real
time in conditionally automated driving and the XGBoost model had the best
performance with an f1-score of 89.1% .

  * •

The obtained results for the real-time prediction of trust provided important
implications for designing trust-aware conditional automated vehicles.

##  II Related Work

###  II-A Factors influencing trust in automation and trust in AVs

A wide range of factors have been identified as antecedents of people’s trust
in automation in general. Schaefer et al. [16] categorized these factors into
human-related, system-related, and environment-related factors. Bashir [17]
proposed another taxonomy and categorized these factors based on three types
of trust on which a factor would have an effect, namely dispositional,
learned, and situational trust [17]. Dispositional trust represents people’s
tendency to trust automation; learned trust is affected by people’s past
experience in using the automation system, and situational trust is dependent
on the interaction between people and the automation in specific contexts. For
a list of the factors influenceing trust in automation, please refere to [16,
17].

With the advances in the development of AVs, there is an increasing amount of
research attention on trust in AVs [18]. For example, Ayoub et al. [1]
identified the factors (i.e., perceived benefits, perceived risks, excitement,
knowledge, and the eagerness to adopt a technology) affecting dispositional
and learned trust using machine learning models. Zhang et al. [19] found that
perceived risk was negatively correlated with initial learned trust while
perceived usefulness was positively correlated with initial learned trust.
With respect to AVs, Zmud et al. [20] reported that safety risks due to system
failures dramatically influenced participants’ trust in AVs, which further
influenced their adoption of AVs. Dzindolet et al. [21] showed that a
transparent system that provided useful feedback and information about why a
failure occured increased participants’ trust in AVs. Luo et al. [22] showed
that participants’ trust decreased more with AVs’ internal errors (e.g.,
sensor errors) compared to external errors (e.g., roadblocks). Okamura et al.
[23] calibrated participants’ dynamic situational trust using calibration
cues. For a list of factors influencing trust in AVs, please refer to [18].

###  II-B Predicting trust in real-time

To objectively measure trust in real-time, researchers argued that
psychophysiological measurements, such as galvanic skin response (GSR), eye
tracking, and heart rate are promising [24, 25, 26]. Eye tracking measures
have been widely used as a real-time indicator of cognitive processes (i.e.,
situational awareness, decision making, and workload) [27, 28]. They offer
many advantages, including non-invasive, unobtrusive, and easy setup.
Recently, researchers have shown that eye tracking data is promising to infer
trust in real time [29, 30]. For instance, a lower monitoring frequency of the
road was associated with higher trust in an AV [24].

GSR has been widely used in measuring anxiety, cognitive workload, and
emotions [24, 31, 32]. GSR is a measure of the sweat-gland activity that
captures arousal based on the skin conductivity. Researchers found that GSR
was correlated with trust. For instance, Khawaji et al. [33] found that
average GSR peaks and values were significantly affected by trust and
cognitive workload. Walker et al. [25] showed that the higher the trust in the
system, the more attention they paid to secondary tasks and the less they
checked the road, and the lower the GSR, and combining GSR with gaze behavior
led to a better prediction of trust. Akash et al. [11] used
electroencephalography (EEG) and galvanic skin response (GSR) and a quadratic
discriminant classifier to predict trust in AVs. The obtained prediction
accuracy was 78.6%.

Heart rate and heart rate variability are cardiovascular measures shown to be
highly correlated with workload, risk, and drowsiness [34, 35, 36]. Heart rate
variability indicates the degree to which the nervous system adapts to
environmental changes through a cardiovascular regulation [37]. During
stressful situations, the sympathetic part of the autonomic nervous system is
activated which results in a sudden increase of the heart activity. HR is
defined as the number of times the heart beats per min and HRV is the time
fluctuations between sequence of successive heart beats. To date, the
relationships between HR, HRV and trust have not been investigated.

##  III Methodology

Fig. 1 provides an overview of the data collection and data analysis
procedure.

###  III-A Participants

A total of 74 university students participated in this study. Due to
malfunction of physiological sensors and the driving simulator, 15
participants were excluded and data from the remaining 59 participants (mean
age = 21.3, SD = 2.9; ranging from 18 to 33; 26 females and 33 males) were
used for further analysis (see Table I). All the participants had a valid
driver’s license and had normal or corrected-to-normal vision. Participants
received $25 in compensation for about an hour of participation. The study was
approved by the Institutional Review Board at the University of Michigan.

Figure 1: Framework of the proposed study. Figure 2: (a) Experiment setup (b)
Trust change self-report question (c) iMotion software.

###  III-B Apparatus and stimuli

The study was conducted using a desktop-based driving simulator from Realtime
Technologies Inc. (RTI, MI, USA). As shown in Fig. 2a, the simulator setup was
composed of three LCD monitors integrated with a Logitech driving kit. Two
other touchscreens (i.e., tablet and phone) were positioned to the right side
of aparticipant for performing an non-driving related task (NDRT) and entering
trust ratings. The NDRT was a Tetris game coded using PyGame library in
Python. The game was designed in a way that the participants were required to
drag the tiles otherwise they did not move. With this design, participants
were able to pause the game to handle the TOR and get back to where they were
before the TOR. A questionnaire designed in Qualtrics (Provo, UT,
www.qualtrics.com) was used to evaluate participants’ trust. A single item
question (“how much do you trust the AV?”) was prompted on the screen every 25
seconds asking participants to rate their trust on a scale from 0 to 10 (see
Fig. 2b). Prompting every 25 seconds was designed bench marking prior studies
[38].

TABLE I: Distribution of participants under the different conditions Control | FA | Misses
---|---|---
Urban | Suburban | Urban | Suburban | Urban | Suburban
16 | 16 | 22 | 22 | 21 | 21

The driving simulation was programmed to simulate SAE Level 3 automation and
participants were able to control the vehicle laterally and longitudinally. To
engage the automated mode, participants needed to press a red button on the
steering wheel. Once the automated mode was engaged, participants would hear
an auditory message, “Automated mode engaged”. Whenever a TOR was issued,
participants would hear an auditory alert “Takeover” and the automated mode
would be automatically disengaged for the participant to take over control. If
the participant did not take over control within the time limit, an auditory
emergency stop warning (“Emergency Stop”) would be issued to avoid any crash.

The Pupil Core (Pupil Lab, MA, USA) eye tracker headset was used to capture
participants’ eye tracking data. The sampling rate of the eye-tracking system
was 15 Hz. The Shimmer3 GSR+ Unit (Shimmer, MA, USA) was used to measure skin
conductance and to photoplethysmogram (PPG) (used to calculate HR) data. Data
from the Shimmer Unit was collected at a sampling rate of 128 Hz. The iMotions
software (iMotions, MA, USA) was used for physiological data synchronization
in real-time (see Fig. 2c).

###  III-C Experimental design

In this study, participants were required to take over from automated driving
eight times. The experiment was a one-way between-subjects design, in which
the participants were randomly assigned to three conditions: 1) the control
condition, i.e., all the eight TORs were true alarms, 2) false alarms, i.e.,
of the eight TORs, the 2nd, 3rd, 5th, and 6th were false alarms, and 3)
misses, i.e., of the eight TORs, the 2nd, 3rd, 5th, and 6th were misses. The
purpose of this design was to elicit different levels of trust since it was
shown that both misses and false alarms degraded operator trust in automation
[39].

Typical roadway features were used when a TOR was issued (i.e., (1) deer
ahead, (2) bicyclist crossing ahead, (3) construction zone ahead, (4) vehicle
sudden stop ahead, (5) pedestrians crossing ahead, (6) bus sudden stop ahead,
(7) construction zone ahead, and (8) police vehicle on shoulder) (See Fig. 3
and Fig. 4). In addition, four takeover scenarios happened in rural areas and
the other four in urban areas. The order of urban and rural takeover scenarios
was counterbalanced.

###  III-D Experimental procedure

Upon arrival, participants were asked to complete a consent form and an online
demographic survey. After the survey, participants received an introduction
and watcheda short video about the tasks they needed to do. Participants then
completed a training session to familiarize themselves with the driving
simulator and the experiment flow. Participants were informed that the vehicle
would be able to perform the driving task when the automated mode was engaged,
but they had to stay alert and be ready to take over whenever requested. We
further explained that the AV could fail to detect obstacles for the
participants to go through the miss condition and that the AV could give false
alarms of TORs for those to go through the FA condition. Next, we calibrated
the eye-tracker device by asking the participant to look at ten targets on the
front screens. After that, we attached the GSR electrodes to the left foot of
the participants and the PPG probe to their left ear lobe. Each drive (i.e.,
urban or suburban) took around 15 minutes and the whole experiment lasted
around 75 minutes. In order to create the ground truth of trust prediction
model, the participants were asked to respond to a single-item trust prompt on
a scale from 0 to 10, “Please indicate your trust level after this encounter”
[24]. Following Desai et al. [38], participants were prompted for this trust
measure every 25 seconds to ensure that they were not overwhelmed.

Figure 3: Takeover events in suburban areas (a) dears ahead (b) bicyclist
crossing ahead (c) construction zone ahead (d) vehicle sudden stop ahead.
Figure 4: Takeover events in urban areas (a) pedestrians crossing ahead (b)
bus sudden stop ahead (c) construction zone ahead (d) police vehicle on
shoulder.

###  III-E Comparison between the three tested conditions

A one-way analysis of variance (ANOVA) showed a significant difference in
trust among the three conditions (i.e., control, misses, and FA),
$F(2,47)=22.323,p<0.001$. A Tukey post-hoc test revealed that trust was
significantly higher in the control ($M=7.967$) and FA ($M=7.699$) conditions
compared to the misses condition ($M=5.597$) ($p<0.001$). There was no
significant difference between the FA and the control condition. Since the FA
condition did not reduce participants’ trust, we did not include it in the
trust prediction model in order to better balance two levels of trust in the
dataset for machine learning.

##  IV Trust prediction model development

Physiological (i.e., galvanic skin response, heart rate, and eye-tracking
metrics) and self-reported data were collected for the trust model
development. To train the trust prediction model, 17 features were extracted
from the data (see Table 2). A five-fold cross-validation was used to optimize
the f1-score of the prediction using a randomized search for hyperparameters.

###  IV-A Data pre-processing

The GSR is composed of phasic (i.e., fast variation of skin conductance) and
tonic (slow variation of skin conductance) phases. In this study, the phasic
component was used since it captures the GSR changes in seconds. Therefore, we
used a continuous decomposition technique (i.e., Ledalab in MATLAB) [40]. The
iMotion software was used to extract the heart rate related measures from the
inter-beat interval. For eye-tracking data, the Pupil Player software was used
for exporting the data collected in Pupil Core for further analysis.

###  IV-B Model features

As shown in the previous sections, our data was collected using different
sensors (i.e., Shimmer and eye-tracking) and systems (i.e., Qualtrics).
Therefore, we synchronized the time between GSR, HR, eye-tracking, and
continuous trust data using the timestamps associated with them. After
synchronization, we used a sliding window of 25 seconds before the self-
reported trust level to extract a series of GSR, HR, and eye-tracking values
within that window. Therefore, each self-reported rating was associated with a
series of GSR, HR, and eye-tracking values at that corresponding timestamp.
The extracted 17 features are listed in Table II based on our previous studies
[41, 42].

TABLE II: Description of the generated features Physiological Data | Model Features
---|---
Heart rate | Heart rate max, heart rate variability, inter-beat interval
Fixation | Number of fixations on the center, left, right, and NDRT screens
Duration | Duration of fixations on the center, left, right, and NDRT screens
Dispersion | Distance between all gaze locations during a fixation on the center, left, right, and NDRT screens
Galvanic skin response | Mean and max of galvanic skin response in phasic phase

###  IV-C Model development

The trust prediction model was trained with an XGBoost model [43] for the
following reasons. First, XGBoost is a decision tree model that combines
multiple trees where each decision tree learns from the previous one to build
a robust model. Second, XGBoost has the power to perform parallel processing
and the advantage to improve the learning process without overfitting. In
addition, XGBoost works with missing data without affecting the model
performance significantly. Third, the XGBoost model can provide feature
importance and help in interpreting the model predictions with the usage of
SHapley Additive exPlanations (SHAP) explainer [44]. The XGBoost model
performance was also compared with other algorithms (e.g., logistic regression
(LR), decision tree (DT), Naïve Bayes (NB), and K-nearest neighbors (KNN)).
The response variable was defined as a binary one (i.e., trust = 1 (trust
value $>=$ 5, sample size = 1745) and distrust = 0 (trust value $<$ 5), sample
size = 484). The objective function used was binary: logistic. The performance
metrics used to evaluate the XGBoost model were accuracy, f1-score, precision,
recall, and ROC_AUC. SHAP explainer was used to explain the predictions made
by the XGBoost model. Specifically, SHAP explainer helped in ranking the
features based on their importance.

##  V Results

###  V-A XGBoost performance

The XGBoost model performance is shown in Table III using 10-fold cross
validation. Also, 10-fold cross validation was used to compare the XGBoost
performance with other algorithms (e.g., LR, DT, NB, and KNN). As shown in
Table III, XGBoost performed the best almost across all metrics (i.e.,
accuracy, f1-score) among the list of machine learning models. Since the
dataset was imbalanced, we varied the sample size of the trust data as shown
in Table IV. In the first trial, we randomly selected the same number of
sample size for the trust and distrust data. In the second trial, we doubled
the sample size of trust data randomly. And in the third trial, we tripled the
sample size of trust data randomly. A 10-fold cross validation process was
used to obtain the results shown in Table IV. The XGBoost model had better
performance with the increase in the sample size of trust data.

TABLE III: Performance measure comparison between different models.

Models | Accuracy | Precision | Recall | f1-score
---|---|---|---|---
Logistic Regression | 79.6% | 79.9% | 98.9% | 88.3%
Decision Tree | 75.7% | 82.8% | 87.1% | 84.9%
Naïve Bayes | 75.1% | 80.5% | 89.9% | 84.9%
KNN | 75.6% | 84.3% | 84.6% | 84.5%
XGBoost | 81.6% | 83.4% | 95.5% | 89.1%

TABLE IV: Summary of XGBoost classifier performance

Sample size | Accuracy | Precision | Recall | f1-score
---|---|---|---|---
Trust (484), Distrust (484) | 74.8% | 72.5% | 79.9% | 75.9%
Trust (968), Distrust (484) | 78.5% | 79.3% | 91.8% | 85.0%
Trust (1452), Distrust (484) | 79.8% | 81.2% | 94.9% | 87.6%
Trust (1745), Distrust (484) | 81.6% | 83.4% | 95.5% | 89.1%

Figure 5: (a) SHAP summary plot (b) SHAP feature importance plot.

###  V-B Feature importance

To understand the importance of each feature in predicting trust, SHAP feature
importance plot was examined. Following our previous study [45, 44], we used
feature selection to identify the best model performance by adding one feature
at a time following the importance ranking of the variables identified in Fig.
5. We continued this process until the f1-score stopped improving. F1-score
was used as our main performance measure since it is a harmonic mean of
precision and recall and better than accuracy when the data is unbalanced for
classification analysis. Finally, we found that XGBoost performed the best
when a combination of seven features (i.e., 1) mean_HR_max, 2) mean_HRV, 3)
number_of_fixations_center, 4) mean_GSR, 5) number_of_fixations_tablet, 6)
mean_dispersion_tablet, and 7) mean_duration_tablet) was used. The SHAP
summary plot shown in Fig. 5a has four characteristics, including 1) the
density that represents the distribution of the features in the data, 2) the
color that shows the range of a particular feature from high (red) to low
(blue), 3) the horizontal variation that shows the large or small effect of
the feature on the prediction, and 4) the vertical ranking that represents the
importance of the feature. Take the feature “number_of_fixations_center” as an
example, a high number of fixations at the center screen led to a decrease in
trust whereas a low number of fixations at the center screen led to an
increase of trust.

##  VI Discussion

The purposes of this paper were: 1) to assess participants’ trust in real-time
in conditionally automated driving under two different system malfunctions
(i.e., misses and false alarms) using physiological measures, 2) to build
different machine learning models to predict trust dynamics in real time in
conditionally automated driving, and 3) to provide implications for the design
of trust-aware conditional automated vehicles. The results of the study are
organized around two main findings. First, we found that XGBoost performed the
best and was able to predict drivers’ trust in real time with an f1-score of
89.1%. Second, we identified the most important physiological measures for
real-time prediction of trust.

###  VI-A Effects of features on trust prediction model

In this study, we proposed a technique to use a combination of physiological
measures, including heart rate activity, galvanic skin responses, and eye-
tracking metrics to predict drivers’ dynamic trust. Previous studies have used
physiological data to measure trust. For instance, Hergeth et al. [24] found a
negative correlation between monitoring frequency and drivers’ self-reported
trust measures. For the feature, “number_of_fixations_center” in Fig. 5a, a
high number of fixations (feature values in red) on the center screen
decreased participants’ trust (SHAP value decreased) whereas a low number of
fixations (feature values in blue) at the center screen increased their trust
(SHAP value increased). As for the feature “number_of_fixation_tablet”, its
effect on trust was not clear as shown in Fig. 5a. Further investigation is
needed to examine this.

For the heart rate related features, a high “mean_HR_max” increased
participants’ trust while a low “mean_HR_max” decreased their trust.
Researchers showed that heart rate increases were associated with stimulus
ignorance and environmental rejection while heart rate decreases were
associated with intake and enhancement of environmental stimuli [41, 46]. It
seemed reasonable that increased heart rate was associated with a higher level
of trust that the participants could ignore the information during the drive
and focus more on NDRTs and vice versa. This measure played an important role
as it was the most important factor as shown in Fig. 5. As for the “mean_HRV”
feature, a high “mean_HRV” was associated with a lower level of trust while a
low “mean_HRV” was associated with a higher level of trust. A higher heart
rate variability was reported to be associated with a lower mental workload
[41, 35]. This might be used to explain that those in the miss condition
possibly reduced their mental workload as fewer TORs were issued. However,
once they realized there were misses, they were more alert to be cognitively
and behaviorally prepared, which indicated that a higher HRV was related to
potentially better cognitive and behavioral scores [47] in a low trust
condition. However, more studies are needed to further validate such
explanations.

With respect to the “mean_GSR” feature, a high “mean_GSR” was associated with
a high level of trust while a low “mean_GSR” was associated with low trust.
Usually a high GSR is associated with a high level of arousal [31], which
might be contributed by the better gaming experience of Tetris of those in the
control condition (a high level of trust) than those in the miss condition (a
low level of trust). In a previous study, Khawaji et al. [26] showed that GSR
values were significantly lower when both trust was high and cognitive load
was low in a text-chat environment. Our finding was inconsistent with theirs,
possibly because trust was coupled with cognitive load in their experiment.
Thus, more studies are needed to resolve such inconsistencies.

###  VI-B Implications

The research findings of this study have important implications for the design
of automated driving systems. Trust is dynamically modeled and predicted using
physiological measures. Such a representation of trust permits continuous
capture of trust over time in automated driving. It thus provides important
implications for the design of a trust calibration system. The system would
help in calibrating the driver’s trust by tracking the moments when the driver
overtrust or undertrust the AV in real-time using physiological data and
machine learning models. Calibration of trust to an appropriate level is
considered a design goal to improve the safety and maximize the benefits of
AVs while avoiding misuse/disuse of AVs [2]. Thus, such a calibration system
might help in improving people’s acceptance and adoption of AV in the near
future.

Moreover, the findings of this study would help advance our understanding of
trust as a determining factor to optimize the interaction between the driver
and the AV system. As shown in the result section, our proposed framework has
good performance in trust predictions in real-time. However, better
performance is needed to be applicable in reality. Our proposed model used
data of a group of users and in reality we can use this model as the base
model and personalize it with individual drivers to further improve its
performance. In this sense, our research paves the way for trust prediction in
real time using more complex models (such as deep learning models) with
physiological data.

###  VI-C Limitations

Our study has limitations that are left for future investigations. First, the
study was conducted in a low-fidelity experimental setup with a desktop
driving simulator. Second, a sample size of 59 participants was considered
small with respect to the three tested conditions (i.e., control, misses, and
FA). In addition, the participants were mainly university students which led
to a homogeneous sample in regards to age, education, driving experience, and
knowledge about AVs. In the future, similar studies should be conducted in a
higher fidelity setup and with a larger and diverse sample size. Third, trust
was mainly evaluated in takeover scenarios in conditionl AVs using drivers’
dynamic trust in control, misses, and FA conditions. In addition, the study
covered a small number of possible takeover issues. Thus, future studies
should explore trust in other scenarios, such as continuous driving
performances in object detection, different types of driving styles, and
recommendations on route selections, and so on. The investigations that
overcome such limitations mentioned above might offer a better understanding
of the relationships between physiological measures and trust in automated
driving.

##  VII Conclusions

In this study, we predicted drivers’ trust in real-time using physiological
data using machine learning models in conditionally automated driving.
Compared to previous studies, our proposed technique was shown to be effective
in estimating drivers’ trust. The XGBoost model had an accuracy of 81.6% and
an f1-score of 89.1%, which outperformed other machine learning models. In
addition, we identified the most important physiological measures for real-
time prediction of trust, including 1) mean_HR_max, 2) mean_HRV, 3)
number_of_fixations_center, 4) mean_GSR, 5) number_of_fixations_tablet, 6)
mean_dispersion_tablet, and 7) mean_duration_tablet. Such a technique has
important implications in the future to guide the design of an in-vehicle
trust calibration system to improve people’s acceptance and trust in AVs.

##  VIII Acknowledgement

This research was supported by National Science Foundation.

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|  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.
---|---
|  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.
---|---
|  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.
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