11institutetext:  1 Laboratory of Innovations in Transportation (LiTrans),
Ryerson University, Toronto, Canada<EMAIL_ADDRESS>22institutetext: 2 Institute of Transport Studies, University of Leeds, Leeds,
United Kingdom<EMAIL_ADDRESS>33institutetext: 3 Laboratory of
Innovations in Transportation (LiTrans), Ryerson University, Toronto, Canada,
<EMAIL_ADDRESS>
August 15, 2021November 17, 2021November 22, 2021 10.17815/CD.20XX.X AX

# Analysis of pedestrian stress level using GSR sensor in virtual immersive
reality

Mahwish Mudassar1  Arash Kalatian2  Bilal Farooq3

(2021)

###### Abstract

Level of emotional arousal of one’s body changes in response to external
stimuli in an environment. Given the risks involved while crossing streets,
particularly at unsignalized mid-block crosswalks, one can expect a change in
the stress level of pedestrians. In this study, we investigate the levels and
changes in pedestrian stress, under different road crossing scenarios in
immersive virtual reality. To measure stress level of pedestrians, we used
Galvanic Skin Response (GSR) sensors. To collect the required data for the
model, Virtual Immersive Reality Environment (VIRE) [1] tool is used, which
enables us to measure participant’s stress levels in a controlled environment.
The results suggested that the density of vehicles has a positive effect,
meaning as the density of vehicles increases, so does the stress levels for
pedestrians. It was noted that younger pedestrians have lower amount of stress
when crossing as compared to older pedestrians which have higher amounts of
stress. Geometric variables has an impact on the stress level of pedestrians.
The greater the number of lanes the greater the observed stress, which is due
the crossing distance increasing, while the walking speed remaining the same.

###### keywords:

Pedestrian Crossing Behaviour Stress Level GSR Virtual Reality Autonomous
Vehicle

††volume: V

##  1 Introduction

It is envisioned that within the next few decades connected autonomous
vehicles (CAVs) will replace the standard car. The goal of CAVs is to reduce
accidents caused by human error, decrease pollution, decrease congestion, and
improve road traffic. Human error when driving causes 90% of accidents, which
will be significantly reduced when autonomous vehicle driven cars are used
[2]. Currently, the advanced features of cars (level 2 autonomous) are cruise
control, parking assistance, emergency braking, lane keeping assistance. Fully
autonomous vehicles can also decrease pollution and congestion, this is due to
cars being able to control the speed and path they travel.

Before the ubiquitous use of fully autonomous vehicles on urban roads, there
is a need for substantial research on their effects on pedestrian, cyclists,
and human driven vehicles. An important research question is to understand how
CAVs will influence the stress levels of pedestrian when crossing the urban
road. Pedestrians walking in urban spaces should be evaluated to determine
their level of comfort with CAVs when crossing signalized as well as
unsignalized intersections. With the study of pedestrian stress, the
adaptation of pedestrian-vehicle interactions can be recorded and evaluated.
The study of pedestrians is extremely important as pedestrian movement and
interaction with CAVs need to be evaluated, to incorporate safety and comfort
features. Research on pedestrian-CAV interactions have started to become more
prevalent in the last couple of years. These recent studies will be one of the
main motivators for safety in urban environments with CAVs.

The contribution of this research is unique, as it evaluates pedestrian stress
and the interactions of pedestrian with CAVs. We used immersive virtual
reality and galvanic skin resistance (GSR) sensor to collect data on
pedestrians in a controlled environment. The data are then analyzed to explore
the effects of various vehicle type, flow, sociodemographic, and geometric
variables on the stress levels. This research will benefit stakeholders, the
government and research agencies as the interactions between human driven and
automated cars will aid in developing new policies and considerations for CAVs
in the market. For government agencies, new policies, procedures and
requirements for the transition of human driven vehicles to autonomous
vehicles are important for the health and safety of the community. The
transition period for these vehicles will take years before automated vehicles
will completely implemented in the market. For stakeholders and research
agencies, this research will give an understanding of how stress impacts
decision for crossing. A pattern can also be found when evaluating multiple
participants on the reaction of stress as well as choices made, during periods
of low, medium and high levels of stress. For researchers this research can be
further developed to understand and implement the safety features for
automated vehicles as well as understand the risks and liabilities for an
automated vehicle.

The next section discusses previous research conducted on pedestrian crossing
behaviour and stress levels. An overview of the experiment and the methodology
as well as data processing is discussed. There is an in depth analysis of
results and discussion which consists of a correlation analysis and
microscopic and temporal analysis of the stress. Lastly, future work and
research are discussed.

##  2 Literature review

When looking at the existing literature on pedestrian behaviour, it was noted
that the data were collected using five primary methods: Virtual
Reality/Simulation, Questionnaires or Surveys, Observational data, Galvanic
Skin Response (GSR) and Electroencephalograms (EEG). Often simulations were
paired with survey questionnaires, so that there was a better understanding of
the participants and data. The topics evaluated during literature review were
child safety, decision making, distracted pedestrians, effects of gender and
age for pedestrians, pedestrian research and street sharing effects of safety
on pedestrian choice.

###  2.1 Child Safety

Children are an uncommon demographic to study when determining pedestrian
behaviour. This is due to children being one of the hardest demographics to
study. This is due to the limits of experimentation with children. Children
tend to treat experiments as a game rather than an experiment, thus they would
perform risky behaviours they wouldn’t otherwise. To resolve this issue,
exposure to the simulation for a few minutes will help children take the
experiment more seriously. This is because children will be able to explore
and have fun in the simulation and it still will be a relatively new
experience when conducting the experiment. It was shown in studies that
children are less observant than their adult counterparts when crossing the
road [3]. When observing children it was discovered that, the more exposure
for a particular experience a person goes through, the less likely they are
stressed. Once adapted to a situation, to achieve the amount of stress before
adaptation, more stimulus is needed [4]. The stress levels of children are
important to understand due to them being the most vulnerable groups to be
injured by autonomous vehicles.

###  2.2 Decision Making

Decision making is an important aspect which was researched in order to
understand pedestrian stress. There are multiple factors that influence the
decision making abilities of crossing. The norms of society and social
pressure have an important role to determine the decisions a pedestrian makes
when crossing. There are different levels of acceptability for pedestrian
actions in different countries. In France, it is more acceptable to cross
while the light is red than it is in Japan [5]. Due to this there is less
stress and lower wait times for pedestrians in France compared to pedestrians
in Japan. Wait times impact the stress level and pedestrian decision making
process. It was noted that if pedestrians wait longer than 40 second on a red
signal, the pedestrian will cross the street despite the red signal [6].
Weather is a significant factor when determining wait time and stress levels
of a pedestrian. When the weather conditions are warmer, pedestrians have
smaller wait times and greater levels of stress [6]. In other studies it was
found that when there were a greater number of lanes, pedestrians were more
hesitant and less willing to cross the road on a red signal. The speed for
walking increased when the number of lanes increase. This is due to the
distance for crossing increasing thus, the walking speed would need to
increase in order to cross the road with the minimum amount of time. With the
increase in the number of lanes, pedestrians were noted to deeply consider
crossing on a red light [7]. The decision making abilities, and stress levels
are varied when there is a crowd or group present. When there is a group of
pedestrians, it was noted that they are more likely to violate the regulations
and rules of crossing [8]. This contributes to the herd mentality which is
common in large groups and crowds. The herd mentality is when a group of
people conform to the belief of the majority in a group. In terms of crossing
behaviour this means that the “leader” of the group makes a decision the rest
of the group will follow [9]. This mentality and group decision making is a
result of social cues that society has formed. When examining the stress
levels of the group “followers”, it was shown that they are more stressed when
crossing the stress as they have not consciously made the choice of following,
but rather the choice was made by the “leader” [9]. The stress levels of a
group versus individual crossing are similar. In an emergency situation
decision making skills and stress levels for an individual were studied. In an
emergency situation, decision making skills are influenced as there are many
factors to consider in a shorter span of time. Pedestrians are known to take
the closest exit route choice available to them as they are more comfortable,
especially when the route is not blocked or crowded [10]. Furthermore, it was
shown that pedestrians are willing to take a further exit if it has
potentially less wait time [11]. When exiting if the exist way is clear of
obstructions then the stress level is relatively normal but with an
obstruction the stress level increases [12]. At a crossing, when pedestrians
are walking with little to no room, the amount of stress increases when
walking [13]. This ultimately means that the more crowded an intersection the
greater the stress levels.

###  2.3 Gender and Age for Pedestrians

The effects of stress levels and wait times can be determined by gender and
age. Males have different biological thinking than women [14]. The reaction to
stress for males and females are also different and thus it affects the
decision making process. It was studied for road crossing when compared to
men, women are more hesitant for crossings [15]. As a result, women are more
stressed than men when crossing. It was noted women would take their time and
cross safely, even if it meant that they had to wait longer. This is due to
the fact women perceive more risk when crossing as compared to men [16]. The
social normality and stereotypes perceived for each sex also alter their view
on crossing and safety. As compared to men, women are told to value rules and
regulations from an early age. This is shown when girls’ hands are held more
often compared to boys when crossing the road [17]. This underlies that girls
are seen as needing protection more than boys, thus internally they would grow
up feeling more cautious with the rules and regulations around them. Age is an
influential factor when determining stress levels and adaptation. It was shown
that senior pedestrians were more likely to underestimate the crossing time
[18]. Therefore, seniors were more stressed out when crossing due to the timer
ending before the senior had time to finish the cross. This is due to seniors
underestimating their physical abilities and their limitations. The most
common underestimation for seniors is gap time, due to seniors having their
speeds slower and mentally assuming their speed is the same as before. For
children the stress levels are higher, this is due to children not having
exposure crossing the road [3]. When experiencing new stimulus, the stress
levels are increased, once there is more exposure the stress levels decrease
[4].

###  2.4 Street Sharing and Pedestrian Choice

Pedestrians and vehicles commonly share the street with one another. Other
factors such as vehicles on the road impact the stress of a pedestrian and
their time of crossing. The effects of sharing the street has an effect on the
stress levels. In one experiment it was shown that when pedestrians were
initially reacting to connected automated vehicles (CAVs), it was difficult
for them to communicate with the CAVs. The interaction and communication
between AVs and pedestrians affected crossing decisions [19]. Due to some
miscommunications, initially with autonomous vehicles, it has caused accidents
between the pedestrian and the vehicle. The interactions were different
depending on age group. Seniors have different interactions and ways of
communication compared to their adult and children counterparts [20]. The
prediction of pedestrian actions in a shared space is important to understand.
This will aid in future safety measures that are in place of autonomous
vehicles. When pedestrians are told which signals to look for in a CAV and the
instructions from a vehicle that is better understood, than vehicle status
[21]. When given directions by the vehicle, it reduced the amount of
miscommunication and accidents. The communication between pedestrians and
vehicles would also be different depending on the country and society, thus
different communication styles would need to be taken into consideration when
designing for CAVs.

###  2.5 Remarks

The current literature related to interactions of pedestrians with CAV is
limited. Some studies have collected the data using using a small number of
participants interacting with a limited CAV e.g., slow moving autonomous
shuttle. Stress levels were not considered rather a questionnaire was
completed and adaptation to the CAVs was measured using the questionnaire. To
the best of our knowledge, no research has monitored the stress level of
pedestrians when interacting with a CAV. This research is unique as stress
levels were measured using galvanic skin resistance sensor when interacting
with AV in virtual reality and adaptation was considered when the same
experiment and conditions were repeated. The change of stress levels were
analyzed from the two experiences. This study explores the effects of stress
levels for crossing behaviour using GSR sensor and virtual reality.

##  3 Methodology

The study focused on mid-block crossing behaviours at unsignalized
intersections. The participant was asked to fill out a questionnaire to
collect social demographic information including age, gender, occupation,
education, driving license holder, number of cars in household, primary mode
of transportation, number of times the participants walk, previous experience
with virtual reality, if the participant struggles with vision, anxiety,
headaches, and dizziness. If the participant revealed that they struggled with
vision, anxiety, headaches and dizziness, they were not proceeded to the main
experiment.

The participant was then asked to wear a VR headset, and a GSR sensor on two
fingers on one hand. The GSR skin conductance was used to measure the stress
levels of a participant. The GSR sensor uses a small electrical charge to
measure the amount of sweat an individual has on their finger. The greater the
charge the greater the sweat. Sweat is a form of arousal which indicates a
level of stress, the greater the sweat an individual has the greater the level
of stress. Thus, the stress levels can be monitored for an individual over a
period of time[22].

The participant was shown a different street scenario in each experiment.
These experiments have multiple variables, which include speed limit, lane
width, minimum gap time (minimum gap time between two vehicles), full braking
before impact, mean arrival rate of vehicles, road type (two way or one way
traffic), day or night, weather, and automation level.The different variables
available for each experiment is shown in table:1. Due to a large number of
combinations possible, a D-optimal design was created and 86 unique
combinations were identified for the experiments. There were a total of 180
participants that participated in the experiment over the course of a 5-month
period. The participants were collected from four main locations in the
Greater Toronto Area (GTA) to include a variety of ages and experiences.
Participants were asked to cross the road when they felt it was safe to do so
and their stress level was recorded alongside the crossing. The participants
x, y, and z coordinates were recorded using the VR system and python coding
which helped determine their changing location. The participant’s
instantaneous speed and acceleration levels were evaluated and compared
alongside their stress levels. The participants that were chosen for analysis
based on a few conditions, such as there were no major health problems, the
participant was able to complete the experiment successfully without any
issues in reading the GSR sensor. For a detailed description and analysis of
the data refer to [23, 22].

Variables | Level of Variables |  |
---|---|---|---
Speed limit(km/h) | 30 | 40 | 50
Minimum allowed gap time (s) | 1 | 1.5 | 2
Lane width(m) | 2.5 | 2.75 | 3
Road Type | One-way | Two-way | Two-way with Median
Breaking levels | 1 | 2 | 3
Arrival Rates(veh/h) | 530 | 750 | 1100
Automation Level | Human Driven | Mixed Traffic | Fully Automated
Time of Day | Day | Night |
Weather | Clear | Snowy |
Table 1: Variables used to define the experiments

###  3.1 Simulation and Setup

The first step of the experiment was to create the simulation with the
variables defined in table:1 using VIRE [1], which was implemented in Unity 3D
with C# programming language. The urban environment and 3D models of buildings
and street fixtures were designed such that they represent a typical road
scene in downtown Toronto. The simulation was projected to an Oculus rift
headset with a 15m extended cable. A GSR sensor was synced with the simulation
in VIRE to collect VR and stress data simultaneously. An empty experiment room
was mapped to the simulation so that the participant can move in it. There was
an area cut off to represent the crosswalk with boundaries. A questionnaire
was created along with a document with all the experiment possibilities.

###  3.2 Data Collection Process

A variety of participants with different experience levels were needed for
this experiment. There were a total of four locations in which the
participants were collected, the locations were Ryerson University, City Hall
(Toronto, Canada), North York Civic centre, and Markham public library. The
participant population at Ryerson University consisted of mostly students. Due
to the location of the university most of the participants commuted, and had
more experience with cross walks. In City Hall and North York Civil Center,
the participants mostly consisted of professionals which ranged from young
professionals to seniors. In Markham, families visiting the library
participated in the experiments. With the collection of a variety of
participants the data were very diverse.

###  3.3 Experiment Details

The experiment started off with the participants filling out the questionnaire
about their experience with crossing walks and their sociodemographic
information. The participant was then asked to place the GSR sensor on their
fingers. The GSR sensor uses sweat to record the stress levels. The sensor
sends an electrical signal which became conductive if there was stress on the
hand. The more emotionally aroused the participant was the greater the stress
levels as there would be a greater amount of sweat on the participant’s hand.
The participant was asked to equip themselves with the VR helmet and complete
a test run of the simulation. To have enough repetitions, each participant was
involved in 15 experiments that lasted 20 to 30 seconds each. These
experiments were chosen uniformly from the 86 unique combinations outlined by
the D-Optimal design. After the experiments were completed, participants were
asked to complete a survey to describe their experience and voice their
feedback. A participant is shown in fig:VRGSR wearing the VR helmet and GSR
sensor. fig:VR2 shows the street scene in VIRE where the experiments were
taking place.

Figure 1: Participant wearing GSR sensor and VR headset

Figure 2: Street scene in VIRE

The GSR sensor data were extracted into a comma separated values (csv) file
format. This format included a lot of different types of values such as skin
conductance and skin resistance being the main focus. Each stress reading was
time stamped to the nearest 0.1 second. The skin conductance was indicated to
be the stress levels as the conductance of the electrical signals were passed
and evaluated using the electrical conductance. One comma separated value file
was given for each participant. Due to every participant completing multiple
experiments, experiments were divided in the csv file. With this division each
experiment was looked into greater depth and stress levels were monitored. The
x,y,z coordinated were extracted from the tracking device and were compared to
the GSR sensor data. When the stress levels and time stamps were matched, the
x,y, and z coordinates were used to calculate speed and acceleration.

###  3.4 Processing of Outcome

Due to the use of multiple forms of equipment, the collected data were in
various formats and units. The VIRE simulator [1] was recording the x, y, and
z axes, every one-tenth of a second. The raw x, y, and z values were first
used to calculate the distance $d$ covered by the pedestrian in one-tenth of a
second using Pythagoras theorem in eq:1. This value was not directly used as
the time interval of one-tenth second was too small and may result in
unnecessary noise in the calculation of speed and density.

$d=\displaystyle\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}+(z_{2}-z_{1})^{2}}$
(1)

Instead, we used one second as out basic unit of time and summed $d$. values
for each second eq:avdis, where $n=10$.

${D}=\displaystyle\sum_{i=1}^{n}({d})$ (2)

Once the average distance was obtained for each second ($\Delta T$), it was
used to calculate speed and acceleration. The equations for speed and
acceleration are shown in eq:3 and eq:4. Where $V$ is velocity, $\Delta T$ is
the time interval of one second, and $A$ is acceleration.

$V=\frac{D}{\Delta T}$ (3) $A=\frac{\Delta V}{\Delta T}$ (4)

In addition to speed and acceleration, we also calculated the cumulative
distance travelled $DC$ for each time interval. The cumulative values were
used to in the comparative analysis of stress. The equation for cumulative
distance is shown in eq:5.

$DC_{t}=\sum_{i=1}^{t}D_{i}$ (5)

Using the GSR sensors, stress measurements were obtained throughout the length
of the experiment. The raw measurements from GSR sensors are intrinsically
noisy and require smoothing operation. We tested several smoothing techniques
and settled on the Gaussian smoothing as it provided the best fit. The
Gaussian smoothing could be used in 2-D and 3-D convolution operators. Most
commonly, Gaussian smoothing is used to remove details and noise in an image,
but it can also be used for data sets to remove excess noise. The equation for
1-D Gaussian distributions is shown in eq:6.

$G(x)=e^{-x^{2}/2{\sigma}^{2}}\times\frac{1}{\sqrt{2\pi}}$ (6)

For this equation $\sigma$ is the standard deviation of the distribution and
it is assumed with Gaussian distribution that the mean is centered around 0.
For our stress levels the Gaussian distribution was centered depending on the
location of the peaks in the stress. The data that was collected fitted the
1-D Gaussian distribution. Using python, a code for a Gaussian filter was used
to smoothing the stress curves. An example of the Gaussian filtering for one
of the participants is shown in fig:gaussians. In fig:gaussians (a), the
original stress levels for Participant 2 Experiment 2 are shown. For
fig:gaussians (b) the Gaussian effect of smoothing is shown. As seen the
smoothing had greatly reduced the noise for the stress levels and is now
convenient to analyze.

Figure 3: Gaussian Smoothing and Stress Level (a) and (b)

The individual stress was also evaluated for each experiment based on the mean
stress level. This is due to mean stress levels having a better indication of
the overall stress of the experiment, rather than maximum or minimum stress
levels. These stress levels were collected and placed into bins to determine
the frequency of each mean stress. The mean stresses were rounded based on the
closest whole value, due to each individual’s stress being a specific value,
which cannot be replicated. As shown in fig:stressdis, the frequency of stress
levels are shown as well as the cumulative percentage of stress. Based on the
cumulative percentage, low, medium and high stress levels were labelled for
each participant and experiment. Low stress levels were between 0 to 20% of
the cumulative percentage, medium stress levels were from 20% to 80% of the
cumulative percentage. The high stress levels included the cumulative
percentage of 80% to 100%. The stress levels were divided into three levels.
Typically cluster analysis could have been used in order to divide into level,
but for the scope of this paper the stress levels were divided using a
20-60-20 split.

Figure 4: Stress Distribution Histogram for All Participants and Experiments

##  4 Results and Discussion

The section is organized into two parts. First an aggregate level correlation
analysis was performed based on the distribution of the variables and the
average stress level of the participants per experiment. All participants were
included and all variables were taken into consideration. It then present the
microscopic and temporal results and analyses of the stress values for several
participants and for different experiments.

###  4.1 Correlation Analysis

A correlation heat map was created with the data collected from the
experiments. Here we pooled the data for all participants. Majority of the
variables described in the experiments were recorded using a binary system.
Since there were multiple combinations of experiments the binary system kept
track of which experiment each participant had completed. Each participant has
completed on average 10-15 variations of experiments, within the allocated
time. One experiment was completed over the course of 15 to 25 seconds. Some
participants were able to complete multiple experiments with the same
conditions, which made it convenient to compare the results and stress levels.
The reaction for each individual to a situation is different, thus when
comparing participant stress levels for the same experiment, this should be
taken into account. The dependent variable in the experiments was the stress
levels, and the independent variables were the sociodemographic and
environmental variables.

fig:corrheatmap shows the correlation heat map for all the variables in the
experiment. This heat map used the average stress for each experiment
conducted for each individual. The stress levels are categorized into 3 levels
i.e., low, medium and high levels of stress. The speed limit is in 3 levels as
well which are 30, 40 and 50 km/h respectively. The minimum gap time is a
value 1, 1.5 and 2 seconds. There are two weather conditions described in this
experiment, one of which is snowy and the other which is clear (clear weather
conditions). The weather condition described would be a binary value (either 1
or 0). This experiment also contained three different road types which were
either two way traffic, one way traffic or two way traffic with median. The
day time was also taken into account, either the experiment settings were
conducted during the day or the night. The age range of the participants were
also monitored to understand the effects of age on the crossing behaviour. If
a participant had a driving licence or their main mode of travel was
transportation that was considered a variable for the experiment as well. It
was asked to the participant if they walked when they went shopping and that
answer was displayed as a binary value. The number of cars owned were also
asked the groups were if one car was owned, one car or more than one car was
owned. The experiment which was conducted determined the level of automation
as well, whether the traffic was completely human driven vehicles, mixed
traffic or completely autonomous vehicles.

From the correlation heat map, it is shown that owning more than one car
caused a positive correlation with stress levels. This means that if a
participant owned more than one car the stress levels for that participant
were higher than the average participant. Stress level also has a strong
positive correlation with gap distance. The greater the gap distance in the
experiment, the higher the stress level for the participants. For negative
correlation, the stress levels are higher for participants ages 50 to 59.
There is negative correlation between the stress level and if the participant
walks to work. This is a logical conclusion as when a participant walks to
work, they have more experience with crossing unsignalized intersection, thus
they would have less stress. As shown in fig:corrheatmap, the correlation
between stress level and owning more than one car is 0.21, which in comparison
to other variables in stress level is high. Between stress level and walk to
work, the correlation is -0.26 which shows a strong negative correlation in
comparison to the other correlation values for stress levels.

Figure 5: Correlation HeatMaps for Variables and Stress Levels

###  4.2 Microscopic and Temporal Analysis of Stress

In this section we focused on the data from three participants to get a better
understanding of the individual stress levels and connections between crossing
behaviour and stress levels. The participants were chosen based on the similar
sociodemographic traits and experiments they performed. All the selected
participants completed an experiment several times, which enabled us to
evaluate the inter and intra participants behaviour and stress measurements.
We used speed, acceleration, stress, and distance from an incoming vehicle to
analyze the data. fig:p1e3 shows these indicators for participant 1, while
indicators for participant 2 are shown in fig:p1e4. The experiment both
participants completed was experiment number 83 out of 86 possible
experiments. The variable values for this experiment included the speed limit
of 40 km/h, lane width of 3m, the road type was a two way road, experiment was
conducted at night during snowy weather, and only human driven vehicles were
in the flow for these experiments.

As shown in the fig:p1e3 for the first participant in experiment 3, the stress
level is continuously increasing steadily throughout the experiment. As the
experiment was continued the stress level increased. When the experiment
reached 14 seconds the stress drops slightly. Overall for this experiment the
participant has an increased amount of stress over time. Speed was calculated
throughout the experiment using the equation in eq:3(a). The speed increased
slightly before drastically decreasing. After that as shown in fig:p1e3, the
speed was slowly increasing and decreasing before increasing drastically. This
suggests to the participant being cautious when crossing the road for this
experiment. Once there was a chance to cross, the participant ran across the
street. The acceleration versus stress graph shows that the participant
crossed the street successfully at the 7th second. After 7 seconds of the
experiment the participant has reached the sidewalk on the other side of the
crossing. This was known due to the acceleration being 0 and continued to be 0
until the end of the experiment had timed out. It should be noted that for
this experiment the respondent’s speed is greater than 0 at the beginning of
experiment, this was because the previous experiment ended and the participant
was still moving a bit. In fig:p1e3 (c), the distance from the pedestrian to
the closest on coming vehicle is shown. As seen, the stress levels increased
when the vehicle was closer to the pedestrian, once the vehicle was shown to
be further away the stress levels decrease.

Figure 6: Participant 1 Experiment 3 Stress, Speed, Acceleration and Distance
from Vehicle (a), (b) and (c)(Experiment combination 83, for the variables of
speed limit of 40 km/h, lane width of 3m, the road type was a two way road,
experiment was conducted at night during snowy weather, and only human driven
vehicles)

As shown in fig:p1e4, the same participant had repeated the experiment with
the same conditions between experiment 3 and 4. As shown the stress levels are
different, despite completing the same experiment. At around 5 seconds since
the start of experiment, the stress drastically decreases for 6 seconds. This
is due to the vehicle distance to the pedestrian being further away at that
time. After that the stress level rose. Based on the participant’s trends it
was shown that the participant’s max stress is less the second time doing the
same experiment compared to the first time. Like the first experiment, the
participant waited for an acceptable gap between vehicles before increasing
his speed and crossing the road. The speed increased over 0.5m/s. As shown in
the diagram the participant was waiting for the “right” opening. The
acceleration graph were also created to determine their relationship with
stress. The acceleration for the experiment shows that the participant
accelerated at one point in time before decreasing drastically and stopping.
In Figure fig:p1e4, it was shown that when the vehicle distance is further
away, the stress levels is lower, but once the vehicle approached the stress
levels increase.

Figure 7: Participant 1 Experiment 4 Stress, Speed, Acceleration, and Distance
from Vehicle (a), (b), and (c)(Experiment combination 83 for the variables of
speed limit of 40 km/h, lane width of 3m, the road type was a two way road,
experiment was conducted at night during snowy weather, and only human driven
vehicles)

For the second participant the stress levels increased drastically, 19 seconds
into the experiment. The participant in experiment 2 had the same conditions
as the first participant in experiment 3. As shown in fig:p2e2, the reaction
to stress is different for this participant. This participant did not have a
gradual increase in stress, but rather has quick moments in which the stress
increased significantly. The speed and method of crossing for this participant
is also different from participant 1. The approach for crossing for this
person in this experiment is to cross the road in segments. This means the
person crossed the first lane when they found an appropriate gap distance
between cars. They stopped at that point for a period of time, until they
found another acceptable gap, result in they increasing their speed. At the
second point the participant increased their speed to 1m/s. Near the end of
the experiment the participant reduces their speed and finished their cross.
This explains why their stress spikes as, when they planned to increase their
speed to cross their stress also increases drastically. The acceleration for
this participant varied throughout the experiment. As shown in Figure
fig:p2e2, after 1 second the distance from the closest vehicle was 100m, which
remained until the end of the experiment. The distance from the vehicle to the
car is only shown for 5 seconds of the experiment. This is due to the data
that was recorded and collected was only analyzed for the last seconds of the
experiment.

Figure 8: Participant 2 Experiment 2 Stress, Speed, Acceleration and Distance
from Vehicle (a), (b) and (c) (Experiment combination 83, for the variables of
speed limit of 40 km/h, lane width of 3m, the road type was a two way road,
experiment was conducted at night during snowy weather, and only human driven
vehicles)

Participant 1 was evaluated for experiment 10, which had different variable
conditions than discussed. The experiment combination of variables was number
56 out of 86 total experiment combinations.This means that the experiment
conditions were as follows, the speed limit was 40 km/h, lane width was 2.75,
minimum gap was 1 second, it was conducted during the day with clear skies and
all vehicles were fully autonomous. As shown in fig:p1e10 (a) and (b) the
speed for this experiment raised significantly at 14 seconds. In comparison to
other experiments by the same participant, it shows that once an opening was
available, the participant used that opening to cross the road. After the
speed reached its peak at 1m/s, it dropped to 0m/s before slowly rising and
coming to a stop. The stress levels for this experiment continuously roused
until the participant reached to the end of the experiment. Compared to the
Experiment 3 the participant’s stress was greater when interacting within
these conditions. This is due to the result of interaction with fully
automated vehicles.

Figure 9: Participant 1 Experiment 10 Stress, Speed, Acceleration and Distance
from Vehicle (a), (b) and (c) (Experiment combination 56,variable conditions
of speed limit of 40 km/h, lane width was 2.75, minimum gap was 1 second, it
was conducted during the day with clear skies and all vehicles were fully
autonomous)

Participant 1 was evaluated for another experiment with the same variable
conditions as experiment 10. This was so that a comparison can be made between
the stress levels and speed when the experiment was repeated. As shown in
fig:p1e11 (a) and (b) the speed, stress and acceleration levels were graphed.
The stress levels dramatically decreased at 2.5 seconds when the speed was at
a local peak. The stress level peaked when the speed was close to zero. Once
the participant’s speed increased drastically to complete the experiment the
stress level also increased. The units of stress are measured in microSiemens
(uS). The maximum stress level for this experiment was 0.14uS which was less
to the maximum stress level at Experiment 10 (0.144uS). Thus, the exposure to
autonomous vehicles a second time, decreased the stress level. Despite this,
the stress levels were overall still high and it can be determined that more
interaction with autonomous vehicles is needed in order for pedestrians to
adapt and communicate with CAVs.

Figure 10: Participant 1 Experiment 11 Stress, Speed, Acceleration and
Distance from Vehicle (a), (b) and (c)(Experiment combination 56,variable
conditions of speed limit of 40 km/h, lane width was 2.75, minimum gap was 1
second, it was conducted during the day with clear skies and all vehicles were
fully autonomous)

Participant 3 was evaluated for experiment 1. This experiment had a speed
limit of 40km/h, lane with of 2.75m, minimum gap time of 1 second, it was a
one way road, taken place during the day when the weather was clear and only
fully autonomous vehicles were present. As shown in fig:p3e1 (a), the speed
increased at 6th second for 2 seconds. This is when the pedestrian started to
walk but stopped. At 15 seconds the pedestrian walked across the street for 2
seconds before drastically walking back. This could be due to an incoming car.
Once the car passed the pedestrian walked quickly across the street and
stopped at their destination. The last 6 seconds of the experiment is shown in
Figure fig:p3e1 (c). This indicates that the vehicle is 100 m away, but due to
the increasing stress over time for the participant, they still feel stressed
until the end of the experiment. This may be due to the adrenaline rush in
their body until the task was completed.

Figure 11: Participant 3 Experiment 1 Stress, Speed and Acceleration,
pedestrian distance to incoming car (a), (b) and (c)(Experiment combination
56,variable conditions of speed limit of 40 km/h, lane width was 2.75, minimum
gap was 1 second, it was conducted during the day with clear skies and all
vehicles were fully autonomous)

Participant 3 was also evaluated for experiment 2. For this experiment the
speed limit was 40 km/h, lane width was 2.75, minimum gap was 1 second, it was
conducted during the day with clear skies and all vehicles were fully
autonomous. The experiment combination of the experiment was 56 out of 86.
This was the same experiment conditions as Participant 3 experiment 1,
participant 1 experiment 10, and participant 1 experiment 11. As shown in
Figure fig:p3e2 (a), the stress levels decreased at 7 seconds then increased
until the end of experiment. The participant’s speed increased as the stress
levels were increasing. The participants speed decreased at 18 seconds and
then increased drastically again until the participant crossed the street
successfully. Figure fig:p3e2(c) shows the last 6 seconds of the experiment.
The stress is increasing as the participant finished crossing the road, as the
participant stress levels was increasing once they were in the middle of
crossing.

Figure 12: Participant 3 Experiment 2 Stress, Speed and Acceleration,
pedestrian distance to incoming car (a), (b) and (c) (Experiment combination
56, variable conditions of speed limit of 40 km/h, lane width was 2.75,
minimum gap was 1 second, it was conducted during the day with clear skies and
all vehicles were fully autonomous)

Participant 3 experiment 3 was evaluated. For this experiment the speed limit
was 50 km/h, lane width was 2.75m, minimum gap was 1.5 second, the road was
one way, it was conducted during the day with clear skies and all vehicles
were fully autonomous. The participant stress levels increased at 14 seconds.
The participant’s stress was drastically increased until they had crossed the
street. The speed of the participant increased and the participant had walked
quickly once and opening between vehicles were found. The distance between the
car and the pedestrian showed that when the vehicle was far from the
pedestrians the stress level decreased, which is shown in fig:p3e3 (c).

Figure 13: Participant 3 Experiment 3 Stress, Speed and Acceleration,
pedestrian distance to incoming car (a), (b) and (c)(Experiment combination
31, variable conditions of speed limit 50 km/h, lane width 2.75m, minimum gap
1.5 second, the road was one way, it was conducted during the day with clear
skies and all vehicles were fully autonomous)

##  5 Conclusion and Future Work

We used immersive virtual reality and GSR sensor to safely collect and analyze
stress of pedestrians when crossing mid-block. The impact of a range of
sociedemographic, flow, vehicle type, and geometric variables on the
pedestrian stress level is investigated. It was shown that pedestrians have
higher stress levels when walking is not their main mode, which may result in
the lack of walking experience on urban roads and lack of awareness.
Pedestrians were also observed to have slightly higher stress levels initially
when interacting with the CAVs. Generally, it was noted throughout all
participants that the stress levels were higher when the distance between
pedestrians and incoming vehicles decreased. When the vehicles were further,
the stress levels were lower. Overall most of the pedestrians had a strategy
to walk quickly across the street once the opportunity for them arose.

We expect that this work will aid in the understanding of pedestrian stress
and their reaction to a range of different conditions on the roads in near
future. This research is beneficial for stakeholders as when implementing
safety features for CAVs, stress levels and reactions of pedestrians can be
accounted for. In the transition period from human driven vehicles to CAVs,
the stress levels were determined for each scenario and can be used to
implement policies and regulations that ensure the safety and comfort of
pedestrians on urban roads. Such measures will also result in the acceptance
of CAVs on roads by the pedestrians and a sustainable and rapid adoption of
this technology.

There are some limitations to this study, which can be addressed in the future
work. The number of distractions for pedestrians can be increased to stimulate
a more life like environment, such as smartphone use and talking to walking
companions. In future work, the analysis of a greater number of participants
will be conducted and empirical models will be used to quantify the
relationships. The stress levels can be further studied by incorporation
different intersection layouts. In future we also plan to study the ability of
immersive VR and simulation environment in capturing the true behaviour,
stress levels, and decision making abilities of the pedestrians in real life.

## Acknowledgements

This research is funded by the Canada Research Chair program, NSERC and
Ontario Early Researcher Award. The funding ID is 2017-11-00038.

## References

  * [1] Farooq, B., Cherchi, E., Sobhani, A.: Virtual immersive reality for stated preference travel behavior experiments: A case study of autonomous vehicles on urban roads.  Transportation research record 2672(50), 35–45 (2018)
  * [2] Khayyam, H., Javadi, B., Jalili, M., Jazar, R.N.: Artificial intelligence and internet of things for autonomous vehicles.  In: Nonlinear approaches in engineering applications, pp. 39–68. Springer (2020)
  * [3] Meir, A., Oron-Gilad, T., Parmet, Y.: Are child-pedestrians able to identify hazardous traffic situations? measuring their abilities in a virtual reality environment.  Safety science 80, 33–40 (2015)
  * [4] Quy, R.J., Kubiak, E.W.: A comparison between “aware” and “naive” conditions in the suppression of gsr activity.  Quarterly Journal of Experimental Psychology 26(4), 561–565 (1974)
  * [5] Feliciani, C., Murakami, H., Shimura, K., Nishinari, K.: Efficiently informing crowds–experiments and simulations on route choice and decision making in pedestrian crowds with wheelchair users.  Transportation research part C: emerging technologies 114, 484–503 (2020)
  * [6] Bendak, S., Alnaqbi, A.M., Alzarooni, M.Y., Aljanaahi, S.M., Alsuwaidi, S.J.: Factors affecting pedestrian behaviors at signalized crosswalks: An empirical study.  Journal of safety research (2021)
  * [7] Shaaban, K., Abdel-Warith, K.: Agent-based modeling of pedestrian behavior at an unmarked midblock crossing.  Procedia Computer Science 109, 26–33 (2017)
  * [8] Brosseau, M., Zangenehpour, S., Saunier, N., Miranda-Moreno, L.: The impact of waiting time and other factors on dangerous pedestrian crossings and violations at signalized intersections: A case study in montreal.  Transportation research part F: traffic psychology and behaviour 21, 159–172 (2013)
  * [9] Faria, J.J., Krause, S., Krause, J.: Collective behavior in road crossing pedestrians: the role of social information.  Behavioral ecology 21(6), 1236–1242 (2010)
  * [10] Mavros, P., Austwick, M.Z., Smith, A.H.: Geo-eeg: towards the use of eeg in the study of urban behaviour.  Applied Spatial Analysis and Policy 9(2), 191–212 (2016)
  * [11] Haghani, M., Sarvi, M.: Human exit choice in crowded built environments: Investigating underlying behavioural differences between normal egress and emergency evacuations.  Fire Safety Journal 85, 1–9 (2016)
  * [12] Li, X., Guo, F., Kuang, H., Zhou, H.: Effect of psychological tension on pedestrian counter flow via an extended cost potential field cellular automaton model.  Physica A: Statistical Mechanics and its Applications 487, 47–57 (2017)
  * [13] Osaragi, T.: Modeling of pedestrian behavior and its applications to spatial evaluation.  In: Autonomous Agents and Multiagent Systems, International Joint Conference on, vol. 3, pp. 836–843. IEEE Computer Society (2004)
  * [14] Kadali, B.R., Perumal, V.: Pedestrians’ gap acceptance behavior at mid block location.  International Journal of Engineering and Technology 4(2), 158 (2012)
  * [15] Holland, C., Hill, R.: The effect of age, gender and driver status on pedestrians’ intentions to cross the road in risky situations.  Accident Analysis & Prevention 39(2), 224–237 (2007)
  * [16] Li, P., Bian, Y., Rong, J., Zhao, L., Shu, S.: Pedestrian crossing behavior at unsignalized mid-block crosswalks around the primary school.  Procedia-social and behavioral sciences 96, 442–450 (2013)
  * [17] Zeedyk, M.S., Kelly, L.: Behavioural observations of adult–child pairs at pedestrian crossings.  Accident Analysis & Prevention 35(5), 771–776 (2003)
  * [18] Oxley, J.A., Ihsen, E., Fildes, B.N., Charlton, J.L., Day, R.H.: Crossing roads safely: an experimental study of age differences in gap selection by pedestrians.  Accident Analysis & Prevention 37(5), 962–971 (2005)
  * [19] Rad, S.R., de Almeida Correia, G.H., Hagenzieker, M.: Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling.  Transportation research part F: traffic psychology and behaviour 69, 101–119 (2020)
  * [20] Cloutier, M.S., Lachapelle, U., d’Amours Ouellet, A.A., Bergeron, J., Lord, S., Torres, J.: “outta my way!” individual and environmental correlates of interactions between pedestrians and vehicles during street crossings.  Accident Analysis & Prevention 104, 36–45 (2017)
  * [21] Ackermann, C., Beggiato, M., Schubert, S., Krems, J.F.: An experimental study to investigate design and assessment criteria: What is important for communication between pedestrians and automated vehicles?  Applied ergonomics 75, 272–282 (2019)
  * [22] Kalatian, A., Farooq, B.: Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning.  Transportation research part C: emerging technologies 124, 102962 (2021)
  * [23] Kalatian, A., Farooq, B.: A context-aware pedestrian trajectory prediction framework for automated vehicles.  Transportation Research Part C: Emerging Technologies (Forthcoming)