# Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and
Techniques

Mohammed Aledhari, Mohamed Rahouti, Junaid Qadir, Basheer Qolomany, Mohsen
Guizani, Ala Al-Fuqaha M. Aledhari is with the Department of Data Science,
University of North Texas, Denton, TX, 76207 USA. Email:
<EMAIL_ADDRESS>Rahouti is with the Department of Computer and
Information Science, Fordham University, Bronx, NY, 10458 USA. Email:
<EMAIL_ADDRESS>Qadir is with the Qatar University, Doha, Qatar. Email:
<EMAIL_ADDRESS>Qolomany is with the Department of Cyber Systems,
University of Nebraska at Kearney, Kearney, NE, 68849 USA. Email:
<EMAIL_ADDRESS>Guizani is with Mohamed Bin Zayed University of Artificial
Intelligence (MBZUAI), Abu Dhabi, UAE. Email<EMAIL_ADDRESS>Al-Fuqaha is
with the Information and Computing Technology (ICT) Division, College of
Science and Engineering (CSE), Hamad Bin Khalifa University, Doha, Qatar.
Email<EMAIL_ADDRESS>received xx, 2023; revised xx, 2023.

###### Abstract

This article outlines the architecture of autonomous driving and related
complementary frameworks from the perspective of human comfort. The technical
elements for measuring Autonomous Vehicle (AV) user comfort and psychoanalysis
are listed here. At the same time, this article introduces the technology
related to the structure of automatic driving and the reaction time of
automatic driving. We also discuss the technical details related to the
automatic driving comfort system, the response time of the AV driver, the
comfort level of the AV, motion sickness, and related optimization
technologies. The function of the sensor is affected by various factors. Since
the sensor of automatic driving mainly senses the environment around a
vehicle, including “the weather” which introduces the challenges and
limitations of second-hand sensors in autonomous vehicles under different
weather conditions. The comfort and safety of autonomous driving are also
factors that affect the development of autonomous driving technologies. This
article further analyzes the impact of autonomous driving on the user’s
physical and psychological states and how the comfort factors of autonomous
vehicles affect the automotive market. Also, part of our focus is on the
benefits and shortcomings of autonomous driving. The goal is to present an
exhaustive overview of the most relevant technical matters to help researchers
and application developers comprehend the different comfort factors and
systems of autonomous driving. Finally, we provide detailed automated driving
comfort use cases to illustrate the comfort-related issues of autonomous
driving. Then, we provide implications and insights for the future of
autonomous driving.

###### Index Terms:

Autonomous driving, Autonomous vehicles, Sensors, Comfort, Driving safety,
Psychoanalysis, Machine learning, Response time, Reaction time

##  I Introduction

Currently, the autonomous vehicles (AV) market is forecasted to reach 186.40
billion USD by 2030, and in 2021 alone, the AV market was worth 4 billion USD.
Such high investments prove that AVs have become the primary revolution in
connectivity technology and automation, and many operations have become
reality [1]. Research on AVs is still developing rapidly since AVs have
emerged to provide travel services for humans. Recent research regarding AVs
is focused on achieving significant improvement in the AV’s safety, fuel
consumption, efficiency, and comfort. Evaluation factors for AVs have also
been examined and implemented in terms of safety and energy, but comfort seems
to be the most difficult evaluation factor to define properly. This brings to
attention a problem within the AV research community: unclear guidelines and
standards on how to provide the best driving experience via a human-machine
interaction system. The comfort of the customer for the AV involves not only
the passenger but also the driver [2]. The AVs are classified from the
automation perspectives into five levels, 0-5. Level 0 refers to vehicles with
no automation; level 5 represents fully autonomous vehicles, where drivers do
not intervene. Level 2 indicates that drivers must control some main driving
tasks while some can be partially autonomously. Vehicles have complete control
to drive autonomously in levels 3 and 4, but drivers are allowed to intervene
if needed. In levels 3 and 4, drivers can manage their time to relax for
specific periods and drive at other times. As such, with each increasing level
of automation, comfort becomes more essential [3].

Comfort can be categorized by different factors as shown in Figure 1. Here,
controllability factors represent the probabilistic attributes linking
accidents to hazardous events based on the likelihood of the driver’s ability
to control the hazardous situation and thus evade harm. Robotic control
factors represent system factors contributing to the movement of the car which
involves the program and mechanical aspects that make it possible to control
the car operations [4], [5], [6], [7]. In general, the comfort level of AV
users is challenging to eliminate or minimize because it is subjective.
Different people will have different levels of comfort. Comfort of self-
driving cars is impacted by many aspects, among which the reaction time of the
self-driving car impacts the comfort of passengers both physiologically and
psychologically [8]. Note that the user’s comfort level includes both the
driver and the passengers of the self-driving car because once it is
successfully implemented, the driver will now become a passenger.
Unfortunately, when it comes to AV planning and design, as far as the
performance of self-driving cars and user comfort of self-driving cars are
concerned, user comfort is treated as an after-thought [9]. The lack of
extensive investigations of control strategies of AVs and their impact on the
comfort factors of AV users and drivers contributed to such significant issues
[10] [11], [12] [13].

e

Figure 1: Crucial concepts to consider for improving AV experience for
consumers.

Further complicating matters regarding comfort for AVs is that there is no
standard definition for it in the scientific community [14]. Some researchers
have categorized the comfort factors into either comfort or discomfort
classes. As such, comfort would simply be defined as the absence of
discomfort. Additionally, [14] the authors discussed whether comfort factors
should be treated as discomfort in the same significance or whether one of
them is more important than the other. This problem is also relevant to the
Human-Machine-Interaction (HMI) areas. Moreover, some literature divided the
comfort factors into three levels of a human’s expressive states, as seen by
Figure 2. This is also applies towards riding/experiencing AVs for the first
time [15], [16]. State A is a negative state, which relates to apprehension of
riding AVs. These are the most complex and essential psychological and
physiological factors for passengers to satisfy the required AV regulations
since it is natural for people to get anxious when they are first using new
devices. Usually, people have a significant level of anxiety if they do not
have prior practice or use for the new technology or device [17], [18]. This
anxiety may accumulate in other forms of discomfort for the passenger [19],
[20]. State B corresponds to the openness (willingness) of that passenger who
wants to use the AV as an ordinary use case, which represents the main goal
for autonomous vehicles. State C represents the positive state (excitability)
when passengers get used to riding with AVs.

Figure 2: A passenger’s emotive state for using AVs based on the concept of
HMI [14].

Figure 3: A roadmap of the paper.

When studying these topics, some researchers focused on a specific type of
discomfort (e.g., motion sickness, anxiety) in relation to AVs. Out of all the
types of discomforts relating to AVs, motion sickness is the most prevalent,
as it has a huge impact on the consumer’s riding experience, acceptance, and
trust in the AV. What causes motion sickness has not yet been conclusively
clarified, but according to a common theory, the cause is likely a sensory
conflict; The AV’s movement and its expectation (what we see) do not match.
According to this theory, it should be able to help give the AV’s occupants as
precise information as possible about the impending movement – acceleration,
curves, braking, and so on. Motion sickness can be identified in several ways,
such as the presence of eye strain, nausea, headache, vomiting, sweating, and
difficulty focusing. Such symptoms can be detected in AV drivers and
passengers by checking and monitoring their physiological conditions. Motion
sickness is one of the active AV research areas, with many strategies and
frameworks being implemented to mitigate to achieve the AV’s maximum comfort.

Safety and security are further crucial factors that affect the comfort of
AVs. In terms of safety, AVs are designed to prevent accidents and minimize
the risk of injury to passengers and road users. The safety features of AVs
include advanced sensors, cameras, and machine learning algorithms that detect
obstacles, pedestrians, and other vehicles in real-time. These features work
together to ensure that the vehicle stays on the road and does not collide
with other objects or vehicles. In terms of security, AVs are vulnerable to
cyber attacks, which may compromise the safety and security of passengers in
the vehicle. For instance, an adversary could take control of the vehicle and
cause it to crash or divert it from its intended route. Thus, safety and
security are critical factors that impact the comfort of AV users [9].
Passengers must feel safe and secure while using these vehicles to enjoy a
comfortable ride. Therefore, manufacturers must prioritize safety and security
in the design and implementation of autonomous vehicles.

This article explores the various possibilities that affect AV user comfort
based on the elements listed in technical aspects, biomedical sensors, and
psychological analysis. It looks at problems that affect the driver’s reaction
time in an incident, the comfort level in AVs, the movement dysfunction,
smoothness, and jerkiness, and whether autonomous driving is relaxing and
safe. Moreover, the article also explores comfort-related factors of
autonomous driving, such as the impact of safety plus self-driving car
architectures. This article not only reviews the comfort of autonomous driving
from a technical perspective but also involves the physiological conditions of
autonomous driving. It also explores the impact and benefits and costs of
autonomous driving from an economic perspective.

###  I-A Scope and Contributions of the Paper

This work reviews and discusses topics of AVs with a focus on comfort and
motion sickness from the humans/users (i.e., drivers and passengers)
perspective. We cover a broad range of technical aspects of both topics in
regards to some current frameworks that have been developed and some common
questionnaires that have been proposed. The paper also covers movement
dysfunction, smoothness, and jerkiness, as those factors impact both the
comfort of AVs for the user as well as the level of motion sickness
experienced. Our main contribution is an exploration of the comfort factors
and motion sickness of AVs from a technical perspective. We examine current
architectures and frameworks proposed for mitigating motion sickness for AVs.
We also examine the correlation between trust and comfort for AVs, as trust is
also a crucial topic of AVs that is still talked about. There has been
discussion about trust in terms of safety and security, but little about
whether trust and comfort are correlated. We provide arguments as to how and
why trust and comfort for AVs are correlated. Unlike existing studies that are
summarized in Table I, the key contributions of this article are:

  * •

Providing a comprehensive and informative overview of the technical and non-
technical elements that impact human comfort in autonomous driving.

  * •

Exploring the different comfort factors and motion sickness from a technical
perspective, examining prominent frameworks and architectures proposed for
addressing motion sickness, and analyzing the correlation between trust and
comfort for AVs.

  * •

Presenting a clear understanding of the challenges and limitations in
assessing and improving the comfort of AV users and drivers, which is a
missing piece of the existing literature.

###  I-B Organization of the Paper

The roadmap and organization of this article are shown in Figure 3.
Specifically, the document structure includes a discussion of similar work in
Section II. These include similar research areas and different aspects of AVs
such as comfort factors, motion sickness, and incorporating trust in the AVs
comfort factors. Then, the article discusses some of the most common
physiological measurements used for comfort in Section III. In Section IV, the
article discusses some current frameworks and representative frameworks that
address comfort, motion sickness, and trust in AVs. Then, Section V discusses
some control process models for enhancing comfort for AVs. Section VI also
discusses some strategies, applications, and implementations done by some
researchers to enhance comfort, trust, and other aspects of AVs. Furthermore,
in Section VII security risks and health issues, best practices in designing
safe AVs, and future research directions are provided. Section VII further
identifies important and growing research opportunities for autonomous driving
computing technology. Last, the conclusion is in Section VIII.

##  II Related Papers

TABLE I: Summary of related works Refs. (Author) |  | Year of
---
Publication
| Comfort
---
Background
| Architectures
---
and Frameworks
| Control
---
Methods
| Comfort
---
Optimization
| Research Implications
---
and Trends
Overview
Cunningham et al. [21] | 2015 | ✓ | ✗ | ✗ | ✗ | ✓ |  | Documented some of the human factors challenges for transitioning
---
from manually driven to self-driving vehicles.
Elbanhawi et al. [22] | 2015 | ✓ | ✓ | ✗ | ✗ | ✗ |  | Tested comfort criteria and discussed some open path planning questions
---
from a passenger comfort perspective.
Koopman et al. [23] | 2017 | ✗ | ✓ | ✗ | ✗ | ✓ |  | Identified the challenges and safety risks associated with inspecting reliable AV systems and
---
how they relate to other safety areas
Fleetwood et al. [24] | 2017 | ✗ | ✗ | ✗ | ✗ | ✓ |  | Reviewed critical public health implications of AV and analyzed the key important
---
ethical issues inherent in AVs design
Crayton et al. [25] | 2017 | ✗ | ✗ | ✗ | ✗ | ✓ |  | Discussed relations between technological innovations in AV and public health risks. Also
---
talks about the relevant health implications of AV policies
Kelley et al. [26] | 2017 | ✗ | ✗ | ✗ | ✓ | ✓ |  | Analyzed public health challenges in the USA and reviews critical factors for determining
---
AV benefits and risks to public safety
Noy et al. [27] | 2018 | ✗ | ✓ | ✓ | ✗ | ✗ | Discussed some AV safety concerns
Morando et al. [28] | 2018 | ✗ | ✓ | ✗ | ✗ | ✓ | Addressed possible safety risks and impacts of AVs as they continue to advance
Burkhard et al. [29] | 2018 | ✓ | ✗ | ✓ | ✓ | ✓ |  | Simulated an AV driving that aims to examine the occupant’s movements in a vehicle.
---
The goal was to address ISO-2621 standard and extend it.
Telpaz et al. [30] | 2018 | ✓ | ✓ | ✓ | ✗ | ✓ | Introduced a new method to identify and classify passengers’ discomforts
Taeihagh et al. [31] | 2019 | ✗ | ✗ | ✗ | ✗ | ✓ |  | Reviewed governance strategies formed in response to AV evolution and discussed
---
technological risks of AVs on different areas
Paddeu et al. [32] | 2020 | ✓ | ✓ | ✗ | ✗ | ✗ | Investigated the potential correlation between comfort level and passengers’ confidence in the AV
Yurtsever et al. [33] | 2020 | ✗ | ✓ | ✗ | ✗ | ✗ |  | Discussed challenges of autonomous driving and covers the technical factors of automated
---
driving. Also covers some issues in high-level system architectures and
methodologies
Rojas et al. [34] | 2020 | ✗ | ✓ | ✗ | ✗ | ✗ |  | Discussed how proper policies and regulatory frameworks need to be implemented to handle
---
AVs before introducing AVs to the market.
Sohrabi et al. [35] | 2020 | ✗ | ✗ | ✗ | ✗ | ✓ | Proposed a framework to systematically identify possible AV health risks
Dam et al. [36] | 2021 | ✗ | ✓ | ✓ | ✓ | ✓ | Conducted a literature review on the new AVs directions and techniques
Guo et al. [37] | 2021 | ✓ | ✓ | ✗ | ✗ | ✓ |  | Experimented the impact of the existing prompt systems of vehicle driving
---
on passenger comfort and motion sickness
Reuten et al. [38] | 2021 | ✓ | ✗ | ✗ | ✗ | ✓ | Discussed correlation between general unpleasantness and specific symptoms for motion sickness
De Winkel et al. [39] | 2022 | ✓ | ✗ | ✓ | ✗ | ✓ | Investigated the potential correlation between the discomfort with motion sickness
Delmas et al. [40] | 2022 | ✓ | ✓ | ✗ | ✓ | ✓ |  | Investigated several AV comfort factors, including road type, weather conditions
---
, and traffic level
Daofei Li et al. [41] | 2022 | ✗ | ✓ | ✗ | ✗ | ✓ |  | Introduced and analyzed a new vibration cue system to alert AV
---
passengers of predicted future AV moving and action
This paper | 2022 | ✓ | ✓ | ✓ | ✓ | ✓ |  | Discussed existing architectures and frameworks that aim to enhance AVs comfort
---
while also discussing several relevant use-cases

Numerous studies attempt to discuss and cover the safety and human comfort
factors for AVs. A study by Noy et al. [27] reviews the promises for improved
safety that are espoused by proponents of automated driving and discusses
considerations in the safety of automated driving, while the work by Yurtsever
et al. [33] discusses challenges of autonomous driving and covers the
technical factors of automated driving. They also discuss some issues in high-
level system architectures and methodologies, including planning and human-
machine interfaces.

###  II-A AV Technological Innovations and Risks

Further work by Rojas et al. [34] and Fleetwood [24] focus on the AV risks to
public safety. Specifically, Rojas et al. [34] discuss the implications and
risks of autonomous driving on public health and reviews policies and
regulatory frameworks related to public safety and transportation, whereas
Fleetwood [24] reviews critical public health implications of AV and analyzed
the key important ethical issues inherent in AVs design.

As the technological evolution in the automated driving industry, the work by
Crayton et al. [25] reviews relations between technological innovations in AV
and public health risks and also discusses the relevant health implications of
AV policies. Similarly, the work by Koopman and Wagner [23] identify the
challenges and safety risks associated with validating dependable AV systems
and how they relate to other safety areas. Additionally, another work by Kelly
[26] analyzes public health challenges in the USA and reviews critical factors
for determining AV benefits and risks to public safety.

Further research attempts such as the work by [35] considered developing a
framework to systematically identify possible AV health risks. This work also
reviewed AV’s impact on public health and safety. Furthermore, the work by
Curto et al. [42] and Morando et al. [28] address the safety implications and
impacts of AVs. Notably, Curto et al. [42] cover the effects and ramifications
of connected and autonomous vehicles on public safety, comfort, and security,
while Morando et al. [28] analyzes the driving safety of AVs using a
simulator.

Taeihagh and Lim in [31] review governance strategies adopted in response to
AV evolution and discussed technological risks of AVs in different areas,
including safety and liability. Meanwhile, Cunningham and Regan [21] identify
and analyze challenges related to transitioning from manually driven cars to
AV and outline requirements to address them.

###  II-B Correlations Between Motion Sickness and Passenger Comfort

The work of Guo et al. incorporates driving condition prompts to alleviate
motion sickness and improve passenger comfort [37]. The authors utilized a
driving simulator to assess the influence of a developed Driving Condition
Prompt (DCP) system on passengers’ motion sickness and comfort. The authors
claimed their experiments could improve passenger comfort when utilizing the
vehicle’s DCP systems and alleviate motion sickness. This system claims to
provide promising enhancement for AV comfort and delivers sufficient
guidelines for future HMI implementations for smart cars.

In another work [41], Li and Chen tackle the challenge of motion sickness,
with semi- and fully automated vehicles. They develop a vibration cue system
that uses tactile stimulation to inform passengers of future AV movement.
Furthermore, this work analyzed some AV simulators’ results of motion planning
that aim to optimize vibration cueing time and patterns. The proposed approach
utilized a vibration cue cushion-based system by evaluating the data and
reactions of 20 participants. The results of the examined system showed a
significant increase in passengers’ understanding of the cues. The proposed
approach claimed the motion sickness of the participants improved
significantly, with results of an average success rate of 89% for generating
motion anticipation.

The work of Reuten et al. [38] also focuses on motion sickness. Specifically,
the authors investigate whether there is a possible correlation between
general unpleasantness and specific symptomatology for motion sickness. The
article highlights a research gap in the symptomatology of motion sickness and
unpleasantness, mainly that the correlation between them is unclear. After
all, motion sickness is shown via feelings of unpleasantness, which range from
slight discomfort to absolute dreadfulness. The authors also argue that there
have been previous studies that have reported positive correlations between
motion sickness symptomatology and measures of unpleasantness. However, the
correlation-based investigation could hide potential deviations. The most
significant manifesting symptom of this issue is vomiting, which is said to
offer relief. For that reason, it will not be fair to rate the bad feeling of
individuals in a similar fashion to how close they are to vomiting. The
proposed work investigated a linear relationship to consider the bad feeling
of passengers as potential symptoms progress. The suggested work divided their
experiments into two phases; 1) the development of unpleasantness and other
signs during continuous motion stimulation; 2) the development of
unpleasantness progression of motion sickness symptoms. The authors studied
the sickness rates in two published experimentations that deployed 20 to
30-minute motion sickness stimuli through virtual and natural motion. Each
test included numerous sessions on different days, and the rates were obtained
at 2 to 5 minutes. The experiment results inferred a positive correlation
between motion sickness symptoms and unpleasantness, but still, there is a
period of relief at the onset of nausea. However, there is a need for further
studies and analysis before the generalization of procedure outcomes that used
only two different scales in addition to the listed anomaly.

The authors of [39] investigated the impact of discomfort factors through
monitoring the motion sickness and associated factors that were expressed in
(Experiment 1) and (Experiment 2). The listed experiments utilized the MISC
test. Experiment 1 employed 15 participants and analyzed the discomfort levels
and associated MISC test results with each level, all participants were males
aged between 20 and 38 years old. Experiment 2 has utilized 17 participants
whose data were analyzed at 60 minutes or until reaching level 6 of MISC.
After those periods, a 10-minute break is given, followed by 30 minutes for
either movement measurements or reaching MISC 6 with a 0.3 Hz oscillation
frequency. After that, participants conducted a second experiment in a
completely dark environment. The MISC at 30-second intervals is used to rate
the level of sickness of participants in each session. In Experiment 1, the
authors found that the discomfort level is increased using the MISC score. The
experiments also found that participants with headaches, warmth, severe
dizziness, stomach awareness, and sweating symptoms are more associated with a
high uncomfortable level. The authors also found that discomfort levels
associated with each level of the MISC increase monotonously in Experiment 2.
While both experiments were in-depth and the results were strong, it should be
noted that for both experiments, the participants were mostly male. The sample
sizes of those experiments could have included more females. It would have
been interesting to see the differences between genders in terms of
discomfort.

The authors of [36] conducted a literature review on some comfort factors
using the PRISMA framework. The above article studied 41 articles for the
period of 2006 to 2021. The literature review employed several keywords to
identify articles discussing motion sickness, causation, and mitigation
methods in human and auto-pilot driving modes. Several databases were employed
in this work, such as IEEE Xplore, EBSCOhost, Scopus, and ACM Digital Library.
Moreover, the mentioned article used some keyword combinations, such as
“autonomous vehicles and motion sickness”, “motion sickness and mitigation and
autonomous vehicles”, “motion sickness and situation awareness and autonomous
vehicles.” The human factors articles represented the majority of the
literature review findings. Also, the above article included vehicle dynamics
and control algorithms research. The article found that most studies utilized
virtual reality (VR) headsets to simulate and collect data on AVs and drivers’
behaviors in non-driving tasks, which cannot help. The findings above utilized
driving routes that include causes of motion sickness. Moreover, some studies
developed heuristic models involving mathematical prototypes for motion
sickness and AV performance that aim to improve the dynamics and offer
comfortable rides. The literature review also concluded that real-world
driving simulators and VR headsets have better reliability than driving
simulators and VR headsets.

The authors in [43] take an interesting approach to investigating motion
sickness in AVs. The hypothesis is that olfaction (which relates to the sense
of smell) may play a role in reducing motion sickness non-invasively. They
focused on special scents, such as lavender and ginger. The test track
investigated the impacts of those smells on drivers and passengers in
chauffeured drives. The results showed that drivers could be at risk due to
those smells in the pre-test and post-test.

###  II-C The Correlation Between Comfort and Trust

The work of [32] has explored the potential connection between comfort and
trust in AVs. The article examined specific characteristics of rider
experiences in the AV environment that impact trust and comfort. The study
involved 55 participants that rode a shared AV under experimental conditions
at a test zone. Each experiment employed 2 participants in four trips,
accompanied by a researcher and safety operative. Two scenarios were presented
for each trip, the direction (forward/backward) and maximum vehicle speed.
Each participant rated the ‘comfort’ and ’trust’ factors after each trip. The
investigation found a significant connection between trust and comfort factors
statistically. Moreover, other studies explored AVs’ trust, such as [44],
[45], and [46].

The work of [40] investigated the comfort factor, considering other
characteristics, such as weather conditions, road type, and traffic. That
study assumed participants drove under level 3 of autonomy using various
situations, including congested traffic, highways, heavy rain, and following
defined speeds. The participants rated their perceived comfort level as they
were driving an AV. The outcomes of the experimentation showed unfavorable
changes in comfort in driving on downtown and highway under various
conditions, such as heavy rain and traffic. At the same time, the experiment
showed that decelerating the car’s speed improved driving comfort. Later, the
experiment studied four profiles 1) adversity to speed reduction, 2) trust in
automation, 3) mistrust in automation, and 4) risk averse.

Comfort in AVs has also been studied using some more novel techniques, as in
the case of[47]. The authors noted that human comfort in AVs is still not
discussed often, and it is a crucial aspect of user acceptance of AVs. The
argument is that existing studies relating to AV comfort only concentrated on
physical factors such as sitting posture, vibration, and noise. Fewer studies
investigated some psychological factors, for which this article aims to
provide sufficient details and discussions. The authors attempt to define
being comfortable as having no discomfort since it has a dominant effect on
human comfort. The authors carried out their study using a driving simulator.
The work investigated the issue in a simulated AV environment. Furthermore, a
collection of AV trips that include motions have been experimented (27 videos
total). The collected data included the physiological signals and comfort
score. The listed work added a pressing button that enabled participants to
express comfortable levels through the press strengths while riding the
vehicle. Pushing the button referred to discomfort, while not pushing
indicated the comfortable. What is unique about this work is that the authors
used wearable sensors to collect physiological signals. The experiment also
involved different types of roads (e.g city roads, highway roads, and
mountain/rural roads) and different types of driving styles (gentle,
aggressive, and normal). The listed videos lasted 3 to 5 minutes, and the
participants were asked to express their feelings after watching each video.
The experiment participants’ feelings were measured and monitored by dividing
the test into many sessions. Each session ran for 75 minutes per day for each
participant. The authors also used a Support Vector Machine (SVM) for comfort
level detection accuracy, and the SVM achieved a 71% accuracy detection rate.
The authors’ experiment was highly detailed and the results were
comprehensive, but the study suffers from sample size issues, a lack of
diversity regarding participants, and generalizability regarding the machine
method used; Similarly, the work of [48] measured sitting position, heartbeat,
discomfort, and skin conductance through deploying sensors.

The work [29] attempts to address comfort in AVs with a different approach.
Unlike the work discussed in this section thus far, the authors actually
address the ISO-2631 standard, which evaluates the impact of environmental
shakes on comfort, health, and efficiency. The authors proposed and
experimented with a modified ISO-2621 standard. This work utilized a body
measurement system (BMS) for documenting occupant movements while driving in
actual and simulated traffic. The used safety head cap is equipped with a
sensor, while another is hooked to the seat rail. The proposed modified model
evaluated the head movement during the experiments. The outcomes of this model
found that measuring the occupant’s head can improve the objectification of
driving comfort for an AV.

The work [30], [49] focused on the discomfort of AV passengers. The authors
assessed the discomfort level by collecting measurements of passenger feelings
while driving in real-time on the road. The main goal of the work is to
identify the passenger discomfort levels using multimodal. The above work ran
image processing techniques using recorded data of the outward-looking camera,
location, and routing to analyze the comfort and discomfort. The discomfort is
defined as adverse changes in passengers’ emotions. Those negative changes can
happen in (1) unwanted body moving, such as a sharp brake, or (2) perceptions
of exterior risks, such as a car being too close to a leading car. The main
idea of the listed work was to recognize potential driving scenarios that may
cause discomfort and classify those scenarios in real-time. Ten sessions of
rides with two participants were conducted in the experiment. Two passengers
were involved in this experiment, one sitting in the front seat and the other
sitting behind the driver. The experimentation was carried out in an urban
area of San Francisco. Participants drove two pre-designed paths using two
distinct driving manners (1) driving in the AV environment with caution and
(2) driving in the non-AV environment without caution. Drivers rated their
driving discomfort and comfort in real-time. The driving was in real-time
measurement using a scale of 1 to 10. One refers to the lowest comfort level,
and 10 indicates the highest level. Also, a logistic regression model was used
to analyze the participant discomfort readings. The experimental results
showed that the perception of risk is critical to a motorist’s pressure and
discomfort.

##  III Physiological Measures for Comfort in AVs

Using various physiological measures for assessing comfort in AVs is
beneficial because they allow for a closer and more objective analysis of the
user for the AV. We can compare several physiological measures against other
potential factors of AV driving. Numerous studies have undertaken this
approach, with a similar consensus being that it may be easier to quantify
discomfort, rather than comfort itself. It is not easy to assess the AV
driver’s comfort precisely because it’s subjective. Moreover, measuring the AV
drivers’ and passengers’ comfort and discomfort in real-time is not pleasant
for them and might result in wrong readings and feelings. So, for measuring
comfort for AV driving, we need non-intrusive and objective discomfort
measurement systems that can be utilized for adapting to the AV’s driving
style and make sure the driver and passengers are relaxed [50], [51].
Thankfully, recent technological advancements such as wearable sensors are
able to give non-intrusive physiological measurements. Some examples of these
are in Table II. Note that the Empatica E4 and Microsoft Band 2 have been used
in multiple studies pertaining to motion sickness and comfort in AVs.

TABLE II: A list of sensors and the physiological measurements they capture. Device | Type | Release Year |  Physiological Signals
---|---|---|---
Apple Watch 7 | Smartwatch | 2021 | BVP, ECG, SpO2
Fitbit Charge 5 | Smartband | 2021 | EDA
Samsung Galaxy Watch 4 | Smartwatch | 2021 | BVP
Huawei Watch 3 | Smartwatch | 2021 | HR
Fitbit Sense | Smartwatch | 2020 | ECG, EDA
Samsung Galaxy Watch 3 | Smartwatch | 2020 | BVP
Apple Watch 5 | Smartwatch | 2019 | ECG
Fossil Gen 5 | Smartwatch | 2019 | BVP
Garmin Fenix 6X Pro | Smartwatch | 2019 | BVP, SpO2
Samsung Galaxy Watch | Smartwatch | 2019 | BVP
Polar OH1 | Armband | 2019 | BVP
Garmin HRM-DUAL | Chest strap | 2019 | ECG
Muse 2 | Headband | 2019 | BVP, EEG, SpO2
Fitbit Charge 3 | Fitband | 2018 | HR
Garmin Vivo Active 3 Music | Smartwatch | 2018 | HR
Oura Ring | Smart ring | 2018 | HR, HRV, SKT
Moodmetric | Smart ring | 2017 | EDA
DREEM | Headband | 2017 | BVP, EEG, SpO2
Polar H10 | Chest strap | 2017 | ECG
VitalPatch | Chest patch | 2016 | ECG, SKT
Emotiv Insight | Headband | 2015 | EEG
Empatica E4 | Wristband | 2015 | BVP, EDA, SKT
Microsoft Band 2 | Smartband | 2014 | BVP, EDA, SKT

The analyzed physiological signals in the study regarding the AV participants
comfort, included the heart rate variability (HRV), systolic blood pressure
(SBP), heart rate (HR), Electrodermal Activity (EDA), and EEG. Some studies
used the Galvanic Skin Response (GSR) and Skin Conductance Level (SCL) as
alternative terms for the EDA [52]. Other physiological measurements such as
eye-tracking have also been used as demonstrated in table III.

TABLE III: Some AV-related studies and physiological measurements used.

Ref. Author(s) | Year |  | Physiological
---
Raw Signals
Measured
Finding(s)
Affani et al.[53] | 2022 | EEG |  | EEG headband - uses to
---
identify
a confidence
of AV drivers
Niermann et al.[48] | 2021 |  | Eye-tracking
---
BVP
| Pupil diameter used to
---
predict discomfort
Gruden et al.[54] | 2021 | EGG |  | Use EGG to identify
---
severity of AV
rider sickness
Dillen et al. [55] | 2020 |  | GSR
---
HR
HRV
Eye-tracking
| GSR and HR - identify
---
and measure the magnitude
of the AV acceleration and jerk
GSR predicted comfort and anxiety
No significant correlations
between trust and skin conductance
regarding passenger trust for the AV
| Radhakrishnan et al.[51]
---
2020 |  | HRV
---
EDA
| SCR is more accurate than
---
HRV-based reading
to scenarios that
induce discomfort
Beggiato et al. [56] | 2019 |  | HR
---
EDA
| Decreased HR during
---
uncomfortable situations
No notable effects for EDA
Lowering eye blinks in
discomfort circumstances,
but pupil diameter increased
Beggiato et al. [57] | 2018 |  | HR
---
HRV
SCL
Eye-tracking
| Reduced eye-blink rate
---
during discomfort
HR- decreased significantly during
discomfort periods
Shahrdar et al. [58] | 2018 | EEG |  | Classified AVs EEG readings
---
using neural networks,
SVM, and MLP
Zhang et al. [59] | 2017 | HR |  | Proposed approach
---
can conveniently
measure HR with
acceptable accuracy

###  III-A Heart Rate Variability and Heart Rate

Heart Rate Variability (HRV) and Heart Rate (HR) and are two types of measures
that have been used in studies involving AVs, driving simulations, and on-the-
road driving studies. With these, both HR and HRV have been used to indicate
levels of anxiety, cognitive measure, workload, and duty demands [60].
However, there is a distinction between HR and HRV; HR is measured in BPM
(beats per minute) and usually does not require exact times. HR focuses on
average BPM while HRV focuses on measuring specific changes in time between
successive heartbeats. The time duration between these heartbeats is measured
in milliseconds and is referred to as the R-R interval or inter-beat interval
(IBI).

Regarding their involvement in AV-related studies, some findings such as those
from [56] and [57], indicated that HR decreased consistently during
uncomfortable driving situations. Similarly, HRV was shown to decrease.
Interestingly, in [55], the authors actually found that GSR and HR were
significantly impacted by driving parameters such as acceleration and jerk,
especially in the presence of another vehicle. These aspects lead to the HR
increasing between 3.6 and 6.5 BPM. In another work by [59], The work
developed a real-time webcam-based detection system to measure drivers’ HR and
monitor their physical activities. The color variations of the skin’s blood
circulation were used to identify the HR.

###  III-B EDA

The autonomic nervous system (ANS) signals are used to track the electric
conductance changes in the skin, which represent the EDA. The sweat gland
activity can be increased using the arousal of the sympathetic ANS activity
that leads to high skin conductivity. For that reason, EDA can be utilized to
measure the physiological arousal readings of participants in response to the
external stimulus [57].

EDA is utilized to monitor and read information for various AV-related
studies, similar to HR and HRV. EDA information includes the workload, task
difficulty, mental effort, skin conductance level (SCL) with higher arousal,
alertness, emotional load, and stress [61]. However, EDA is not a reliable
metric due to its sensitivity to various stimuli. The increase in SCL readings
would be expected due to higher alertness and arousal prediction indicating
discomfort. There have been some notable findings with this physiological
measure as demonstrated by [51], where the authors found that EDA values were
more sensitive than HRV values in terms of scenarios that induced discomfort
in AVs. Other examples come from [44], [46], [52], [62], and [63], which
examined the correlation between EDA and trust, where a high level of EDA
refers to a low level of trust. However, these claims were disproven in [55],
where the authors indicated there were no significant correlations between
trust and EDA. A similar case also occurred in Julius Hörsting et al. [45],
where the authors compared EDA among three groups in regard to trust and EDA
for AVs. The goal was to investigate the correlation between AV speeds and
participants’ trust. The EDA was used as the physiological measurement. The
study didn’t find a significant correlation between the three groups using the
EDA readings.

###  III-C EEG

An electroencephalogram (EEG) allows us to measure the electrical activities
in human brain. Applying electrodes to the individual’s scalp is the way to
get EEG readings. EEGs have been used in AV studies for a few aspects, mainly
emotional state and stress; One example comes from [58], where the authors
incorporate EEG as an indicator of the emotional state of a passenger in an AV
simulator. Another example comes from [64], where the authors hypothesize that
abnormalities in braking timing for AVs were reflected in the physiological
signals. Upon experimenting and evaluation, the authors’ results indicated
that it was in fact possible to estimate the driver’s abnormal braking by
using the physiological signals before and after the braking timing. In [53],
the authors developed a wearable EEG headband to measure stress-related brain
activity during driving. Upon testing the proposed approach on 10 volunteers,
results showed that manual driving had the highest stress on drivers.

Perhaps the most notable work that uses EEG comes from [65], where the authors
focus on designing a complex sensor for AVs, by equipping a semi-AV with an
intricate sensor structure that can provide centralized data regarding EEGs of
both passenger and driver of the AV. This would in turn transform the AV into
a mobile sensor connected via the Internet. The authors design the proposed
sensor such that the acquisition devices of EEG signals are installed in the
head and backrests on car seats. Not only does this allow for maximum comfort,
but the approach can be used as a way to help the transition between manually
driven vehicles and AVs by bringing forth a system that monitors the well-
being of the driver’s location in AV and non-AV environments.

###  III-D Other Physiological Measures of Comfort

Some other physiological measures of comfort for AVS involve eye-tracking.
Studies by Drewitz et al. [66], Dillen et al. [55], and Beggiato et al. [56]
[57], all incorporated eye-tracking when assessing for comfort/discomfort. The
work found that the diameter of the pupil is primarily on ambient light and
depends on mental states. Niermann also found that the pupil diameter is the
best factor for predicting and evaluating AV drivers’ discomfort [48].
Additionally, Beggiato et al. found that there is a reduced eye-blinking rate
during discomforting AV scenarios. Furthermore, [67] explores the possibility
of incorporating face-tracking strategies to monitor comfort for driver state
monitoring regarding discomfort in AV driving. Another technique, [54], was
used to identify the potential correlations between increased amplitude
recorded EGGs and the severity of self-reported sickness.

##  IV Architectures/Frameworks Addressing Comfort, Motion Sickness and Trust

###  IV-A Motion Planning Algorithm

Many researchers have proposed various frameworks that address comfort and
motion sickness for AVs, with one example coming from [68], which proposes a
novel concept to remediate car-sickness problems in motion planning, instead
of motion control. This work uses a frequency shaping method to develop a
motion planning algorithm that is commonly used for checking automobile
sickness. The outcomes of the simulation and experiment showed a promising
reduction by 21% and 37% of motion sickness dose value (MSDV). The ISO
2631-1:1997 standard referred to the used MSDV-based motion planning
algorithm. The MSDV is defined as follows:

$\begin{array}[]{c}{MSDV\ =\
\sqrt{\mathop{\smallint}\nolimits_{0}^{T}{{\left[{\tilde{a}\left(t\right)}\right]}^{2}}dt}}\end{array}$
(1)

$T$ refers to the total exposure time of the acceleration and the stimulus
acceleration weighted by function, as defined in ISO 2631-1:1997.

Figure 4 [68] shows the formulated motion planning algorithm similar to the
optimal control problem (OCP). The planned vehicle trajectory is determined by
minimizing the cost obtained by calculating the route planner’s target
positions and velocity profiles.

Figure 4: Illustration of the motion planning architecture [68].

The actual mitigation happens in the motion planning phase, so there is no
impact for the motion control to cause motion sickness. The need for fewer car
parameters is a strength of the proposed approach, making it a sufficient and
preferable algorithm for motion planning.

The proposed algorithm is designed to work in real-time, which requires
initial and final conditions. The experiment set the initial speed vehicle to
1.7m/s for simulated congested urban driving conditions and 30 km/h for the
main road. The experimentation assumed the vehicle was equipped with the
optimal control algorithm to keep the car at the planned velocity on the
intended path. The experiment included comparing the proposed framework to a
polynomial-curve-based planning methodology, a benchmark algorithm. A
polynomial-curved-based method is widely used for AV-based research. The
polynomial function and time are used to calculate the AV path and velocity.
The polynomial function parameter is defined in two steps and evaluated using
the Root Means Squared Error (RMSE). The authors claimed that the experimental
results showed that the proposed framework performed better performance than
the benchmark algorithm in identifying comfort. They also stated that their
method increased sickness alleviation by up to 37%.

Overall, motion planning algorithms can improve comfort by providing a smooth,
predictable, and customizable ride experience based upon the following
features.

  * •

Optimizing the trajectory to ensure that the ride is as smooth as possible and
minimizing sudden accelerations, decelerations, and turns. This can alleviate
the discomfort and motion sickness.

  * •

Anticipating the behavior of other road users and adjusting the trajectory
accordingly. This helps avoid sudden stops and maneuvers, reducing the
likelihood of discomfort.

  * •

Providing customizable driving preferences to take into account the driving
preferences of individual AV users. For instance, if a user prefers a more
conservative driving style, the AV can be programmed to adjust its trajectory
accordingly.

  * •

Providing an adaptive ride comfort by adjusting to changes in the environment,
such as road conditions and traffic patterns. By adjusting the trajectory in
real-time, the motion planning algorithms can ensure that the ride is as
comfortable as possible, even in challenging conditions.

###  IV-B XR Mobility Platform - For Passenger Comfort Improvement

A multi-modal system introduced in the [69] could be mounted on the AV to
improve passenger comfort. The work claimed that the drivers were freed from
driving and became passengers, and the windshield and windows became
dashboards to provide driving information. The goal was to implement
technology for improving the passenger’s comfort during auto-driving. A
concept covered in this framework is comfort intelligence (CI), which focuses
on passenger comfort in AVs and on both the positive and negative states of
the passenger’s feelings inside the AV. The authors claimed their approach is
better than other approaches, particularly because it uses immersive displays
with a controllable seat capable of tilting in all directions in the actual AV
environment. The proposed method architecture is shown in Figure 5.

Figure 5: XR Mobility Platform Infrastructure [69].

The proposed framework includes several elements, as shown in Figure 5. The
Head Mounted Display (HMD) incorporates a high presence of VR inside the AV,
and the dashboard camera records images. The passenger seat of the minivan’s
backspace equipped with the motion platform is used to monitor the motion.
Numerous actuators are connected to the back of that seat to control tilting
through a computer. The proposed framework also uses an open-source software
called Autoware.AI, which deals with driving control. Autoware.AI gets
behavioral data of the AV from sensors through a controller area network (CAN)
bus.

Furthermore, the proposed framework also uses two control methods to improve
passenger comfort. The first one focuses on reducing car sickness. This
strategy is supposed to reduce sensory conflict among the passenger by
controlling the passenger’s body movement directly. So, both the cylindrical
screen and motion platform seat are tasked with reducing acceleration stimuli.
With this approach, the passenger is supposed to feel fewer acceleration
stimuli and what they do feel is similar to the driver’s movement. The second
control strategy focuses on reducing passenger movement. This second approach
utilizes the HMD to control the visual movement and body of the passenger. The
HMD used is the immersive HMD that is attached to the passenger’s head. This
helps creates an illusion that the passenger does not feel they are tilting,
which helps the passenger feel less movement overall.

Overall, the XR Mobility Platform has the potential to enhance passenger
comfort by providing a more immersive, personalized, and interactive
experience for passengers based on the following aspects.

  * •

VR simulations for different transportation scenarios, allowing passengers to
experience their journey before it even begins. This can help to reduce
anxiety and uncertainty, which may cause discomfort for some passengers.

  * •

Passengers can create personalized virtual environments that suit their
individual needs. For instance, they can adjust the lighting, temperature, and
sound to enable a more comfortable environment.

  * •

Real-time information for passengers about their journey, such as the location
of their vehicle and any delays or disruptions. This helps reducing stress and
improve the overall passenger experience.

  * •

Interactive entertainment options for passengers, such as virtual games and
movies, which may help to pass the time and distract them from any discomfort
they may be experiencing.

###  IV-C Simulation Framework for Assessing Ride Comfort

A simulation framework to evaluate comfort using collected data from vehicle
dynamics has been proposed in [70]. The proposed simulation framework also
generates a process for producing optimal comfort estimates by incorporating a
Monte Carlo simulator and road surface model. The authors then develop a case
study with the proposed simulation framework. The case study consists of a few
real road sites that demonstrate the effectiveness of the proposed approach
with actual data, and is able to achieve highly optimal comfort results.

The work utilized specific frequency ranges that follow the ISO-2631
application domains to provide accurate comfort readings. As such, low
frequency is between 0.5Hz and 80Hz in terms of health, comfort, and
perception. Motion sickness varies in range from 0.1Hz to 0.5Hz; The proposed
technique utilized a frequency weighting approach based on the ISO-2631
standard to identify frequency ranges and specific applications. The frequency
weights have been selected based on the passenger position and the application
type (i.e., sitting, standing, or recumbent). The overall conceptual
architecture of the proposed method is shown in Figure 6.

Figure 6: The proposed simulation framework generates a process for producing
optimal comfort estimates by incorporating a Monte Carlo simulator and road
surface model [70].

The simulation framework is shown in Figure 6. An XML file is used to save the
framework’s configuration parameters, which provides flexibility to specify
various test scenarios. The input parameters of the corresponding
probabilistic are shown in block 1, and the automation functionality and the
communication interface to the simulation environment are presented in block
2. The framework has been implemented using MATLAB/Simulink setting. MATLAB
Simulink and CarMaker are both used as simulation software for set-up, input
configuration, and data exportation (block 3). Block 4 included the dataset
that utilizes to quantify comfort. Several methods are applied, such as bi-
linear transformation, signal filtering, and final calculation. Finally, block
5 shows the storage of the comfort prediction. The authors chose to go with a
generic framework so that input or output parameters can be easily replaced by
different technologies. Moreover, other simulators compatible with
MATLAB/Simulink can be used for the framework simulation instead of CarMaker.
These changes will not negatively impact the performance of the proposed
approach. The authors reported that their proposed simulation framework found
a relationship between the acceleration signal peaks and comfort.

###  IV-D A Hardware-Software based ELM Approach for Improved Ride Comfort in
AVs

The authors in [71] also focus on ride comfort. More specifically, the authors
propose a hybrid hardware/software approach to improve AV ride quality. In
addition, a driving style identification system is also integrated, as driving
style is also taken into account in the proposed work. The proposed approach
is utilized on a one-chip implementation to read driving comfort levels in
real-time. So, the proposed framework categorizes the comfort-driving data
into several types. The experiment used hierarchical cluster analysis (HCA)
(unsupervised learning approach) and supervised extreme machine learning (ELM)
to improve performance. HCA uses a bottom-up approach to measure the distance
between two clusters, then merges the following two clusters at the next step.
The Euclidean distance is used to evaluate HCA performance, similar to the
nearest neighbor algorithm. The proposed framework uses Xilinx’s Zynq-7000
programmable system-on-chip for real-time data processing and determining
comfort levels. It also operated high operating frequencies and low latency
times. The goal of this proposed approach was to alert drivers whenever
passenger comfort was compromised.

This framework is featured by detecting non-continued circumstances, such as
sudden lane shifts and moves that cause motion sickness and discomfort,
especially if they are maintained over an extended time. The proposed work
identifies the discomfort by recording the highest acceleration and jerk
speeds, usually when aggressive lane changes occur. The discomfort can happen
when values of acceleration and jerking are high, even for a short period.
Figure 7 shows the framework components that handle the data exchange between
the AV’s CAN bus and the entire system. The driving style can be determined
using the ELM-based model. To enable the system to work correctly and
precisely must carefully follow specific configurations to integrate hardware
with software. The proposed framework also utilized a dual-core ARM
microprocessor with peripherals, so the software can run on a full-size FPGA
where the ELM is deployed.

Figure 7: Hardware-Software based ELM approach for Improved Ride Comfort in
AVs [71].

The experiment of the proposed approach showed a 95% success rate of comfort
after classifying driving data. The authors claimed that the proposed
framework is helpful, especially the lack of driver advising-based comfort-
improving solutions. They also indicated it could easily integrate the
proposed approach with current auto line productions with some minor
modifications to increase the comfort level of AV driving.

###  IV-E Lateral Control Strategy based on Head Movement Responses -
Mitigating Motion Sickness

A new control strategy has been proposed to mitigate AV motion sickness, which
uses head roll angle as a control variable to reduce lateral acceleration
[72]. The authors first develop a base model for driver and passenger head
roll. The models of a driver and passenger are viewed as control subjects.
This strategy employed a fuzzy logic control and head roll angle response to
induce a corrective wheel angle. The proposed approach was run through via
simulations, which showed a 3% to 10% reduction in motion sickness.

Overall, lateral control strategies based on head movement responses are an
effective way to mitigate motion sickness in autonomous vehicles by reducing
the sensory conflict that causes it. By aligning the visual field with the
physical motion of the vehicle, passengers can enjoy a more comfortable and
enjoyable ride experience. For instance, if the vehicle turns left, the
display can be shifted to the right, creating a compensatory visual stimulus
that aligns with the physical motion of the vehicle. This mitigates the
sensory conflict between the passengers’ visual and vestibular systems, which
is the primary cause of motion sickness. It is crucial to note, however, that
this strategy may not work for all passengers and that individual differences
in susceptibility to motion sickness should be taken into account.

###  IV-F A Device for Predicting Motion Sickness in AVs

In [73], the proposed framework is actually a device that could be used to
predict user motion-sickness in real-time using motion, head tilt, and ambient
conditions. The proposed approach heavily focuses on Non-Driving-Related-Tasks
(NDRTs). Requirements for the device were the following:

  * •

Objective motion dose score

  * •

A subjective sickness score

  * •

Ambient temperature sensitive

  * •

A position of the occupant head’s

  * •

Variability of the sensitivity tuning

  * •

Real-time display and calculation

The introduced model aims to capture the AV acceleration, using an Arduino
microprocessor and an Inertial Measurement Unit (IMU) equipped with an OLED
display. The model reads a single-user trial of over 100 samples from more
than 1500 experiments to identify a subjective sickness score. Figures
detailing both the motion sickness measurement device and the device software
schematic are shown in Figure 8.

Figure 8: the proposed framework is actually a device that could be used to
predict user motion-sickness in real-time using motion, head tilt, and ambient
conditions [73]. Part (A) Represents the Hardware schematic and Part (B)
Represents the Device Software schematic

The proposed device also featured detecting temperature besides motion
sickness signals in real-time. Furthermore, multiple journey routes were
computed during the data collection and testing phases, with more than 30
minutes for each journey. The entire experiment conducted 101 trips. The
samples were collected from enormous 14-seat mini-buses and passenger coaches.
The authors also used data from multiple seating positions. The vehicle’s air
conditioning system controlled the ambient temperature. The authors also
conducted testing in partly cloudy weather.

Overall, devices that can predict motion sickness can potentially improve
comfort in AVs by enabling advanced warning to users and allowing them to take
preventative measures. These devices employ a variety of sensors to track the
vehicle’s movements and the passenger’s physiological responses to them. For
instance, they may assess the user’s heart rate, skin conductance, and other
indicators of motion sickness. This information can then be analyzed by
algorithms for predicting the motion sickness occurrence likelihood. Upon
detecting a high risk of motion sickness, the device can alert the user and
provide suggestions on how to prevent it. For instance, it may suggest
adjusting the temperature, opening a window, changing the seating position, or
taking medication. This can help take proactive measures to prevent motion
sickness before it becomes severe.

In addition to architectures that address comfort and motion sickness, there
are also architectures/frameworks which address the trust aspect of autonomous
vehicles. Such examples of these frameworks are discussed in the next few
subsections.

###  IV-G Sense–Assess–eXplain (SAX)

Numerous assurance and regulation critical barriers to deploy large-scale
deployment of AVs were raised and discussed with their potential solutions in
[74]. The work attempts to address and clear the following objectives with the
proposed approach:

  1. 1.

Reading and analyzing weather conditions on multiple scenarios to test
potential sensing modalities’ limitations.

  2. 2.

Continually evaluating and optimizing the perception performance and
navigation methods.

  3. 3.

Demonstrating the system’s capability in interpreting the vehicle vision and
how that impacted its decision-making.

To satisfy these objectives, the authors develop a model known as sense-
assess-eXplain (SAX). The proposed method is used to measure assurance and
trust in AV operations. SAX comprises three core research aspects that aim to
identify and match various levels of AV sensing and evaluation, which helps
understand vehicle observations and decision-making in real-time. The AV can
also clarify that the current and predicted environmental conditions impacted
its performance. Figure 9 illustrates the overall schematic of SAX.

Figure 9: The developed sense-assess-eXplain (SAX) model [74]. The proposed
model is used to measure assurance and trust in AV operations.

###  IV-H Real-time Trust-Building Schemes for Mitigating Malicious Behaviors

The importance of trust regarding AVs safety and security was discussed in
[75], which also suggested two architectures: centralized and distributed, to
reduce dangerous behaviors using AVs trust measurements. Those architectures
divided the roles of the trusted authority and vehicle nodes in plausibility
checking and sustaining trust to detect trust in real-time. The suggested
method (shown in Figure 10) also assumes the adoption of a public-key
infrastructure to facilitate inter-vehicle communications, in which case a
trusted authority is in charge of issuing and revocation of certificates for
certifying AV IDs.

Figure 10: The two architectures developed in [75]—(1) centralized and (2)
distributed—to reduce dangerous behaviors using AVs trust measurements.

The centralized architecture utilized the trusted authority to monitor vehicle
functions and interchanges via V2X messages within a pre-defined area. Each
vehicle was collected and transferred broadcasted V2X messages to the trusted
authority for verification. Figure 10 illustrates how the trusted authority
communicates and handles the authenticated vehicle identity and message
integrity and calculates trust. The capability of the authorized source to
have a broader view of each AV’s behaviors concerning time duration and travel
distance is the main advantage of the centralized architecture to detect
malicious behaviors. Nevertheless, storing trajectory and running plausibility
checks of each vehicle increase memory consumption, a significant weakness of
this architecture that needs to be considered when deploying an existing
system in AVs.

In contrast, vehicle trajectory verification is delegated to AVs in the
distributed architecture, as shown in Figure 10 (B). A car verifier estimates
the vehicle’s location and then decides the AV trust value and shares data
with the trusted authority. In that way, the trusted authority only updates
its trust on the related node. This architecture’s strengths are detecting
local malicious behaviors that are out of vehicles’ signal transmission and
reducing memory consumption and associated calculations. However, this
architecture’s weakness is the vehicle verifier’s inability to detect
attackers on a global scale.

The listed work modeled a Python-based simulator for non-continued events to
evaluate V2V messages exchange processes among several units. This work used
centralized architecture to implement verifiers for trusted authority, while
distributed architecture was developed for AVs. Moreover, four existing
algorithms have been utilized in this model to test the plausibility checking
module. The open-source dataset called VeReMi was used to assess malicious
behaviors algorithms detecting in vehicular networks. Both centralized and
distributed architectures have been deployed to test and evaluate four
malicious strategies under low- and medium-density states. Despite the poor
performance of the listed methods, they represent a good choice to continue
investigating and gain user trust during driving AVs.

###  IV-I Safe and Online MPC

The AV navigation system that uses MPC controller design was presented in
[76], containing longitudinal and lateral controls. Besides the safety
monitoring module, it also has a cost function designed to calculate human
driving habits. It estimates the required time to reach unacceptable
situations, including tracking performance and comfort constraints under
different tracking conditions. The strength of this kind of controller is
presented in its ability to guarantee smooth trajectory and acceptable
performance. Moreover, a simulator has been implemented to test the proposed
MPC controller under typical urban settings.

The initial Guidance, Navigation, and Control (GNC) architecture components
helped develop the suggested MPC controller to facilitate navigation and
wayfinding in urban areas. The listed MPC controller controls both axes, the
lateral and longitudinal. The lateral control signal manages the car steering
via passing signals to a low-level controller. Calculating associated risks
with the current tracking condition is another task of the MPC controller,
which is considered a safety monitoring algorithm. This risk was calculated
using the predicted lateral error and MPC accuracy. The proposed architecture
is shown in Figure 11.

Figure 11: The proposed AV navigation system that uses MPC controller,
containing longitudinal and lateral controls [76].

The suggested model was developed and examined using the 4D-Virtualize
simulation engine and Robot Operating System (ROS) framework to provide semi-
actual vehicle experiences. The simulation utilized IPCar, an urban electric
vehicle with a maximum speed of 3 meters per second and a turning radius of 3
meters. The presented technique is robust to noise and is expected to solve
ground vehicles’ trajectory-tracking issues. Additionally, the proposed work
also considers GPS data, making sure the GPS data is accurate as well.
Furthermore, the proposed approach also achieved lower complexity. A strong
benefit of this lower complexity would be that the AV gets an easier time
planning its destination/route so that the passenger discomfort is potentially
minimized.

##  V Intelligent Control Methods and Optimizations

When working for control process models and robust machine processing
implementations for enhancing comfort and trust for AVs, intelligent control
methods contrasted for conventional control methods are largely expressed.
These include model-driven control systems, neural network management and deep
learning methods [77], [78], [79]. Such algorithms are currently being used
slowly in widely used automotive regulation. Figure 12 presents an overall
taxonomy of the application of these intelligent control methods.

###  V-A Model-based Control

Model-based control is also known as Model Predictive Control (MPC), Rolling
Horizon Control (MHC), and Receding Horizon Control (RHC). It is a model
prediction-based computerized control system, which has been extensively
researched and applied over the last few years [80] [81]. The basic theory can
be summarized as: an open-loop optimization problem of the final time domain
is overcome at each sampling point and the first dimension of the control
sequence obtained is implemented on the controlled object according to the
current measurement information currently obtained. Invoke the above procedure
in a sampling instance and then restart and address the question of
optimization with new calculated values. The key distinction between
predictive model management and conventional control methods is to solve open-
loop problems digitally in order to achieve open-loop optimization sequences.
The predictive monitoring algorithm consists primarily of four parts:
predictive model, adjustment of feedback, rolling optimization, and comparison
trajectory. The first (or first part) aspect of the optimization approach is
better added to the method.

Figure 12: The potential integration of AI into control models (fuzzy logic,
evolutionary algorithms, and ML) with a summary of optimization applications
for driving comfort enhancement [82].

###  V-B Neural Network and Imitation Learning Control

Neural control is the study and usage of certain structural mechanisms of the
human brain and the control of the system by human knowledge and experience
[83]. The control problem can be considered to be a pattern recognition
problem using neural networks, and the mode defined is a “shift” signal which
is transformed into a “compassionate” signal. The most remarkable feature of
neural control is its ability to learn and adapt. It is achieved by constantly
correcting the weight connections between neurons and storing them discretely
in the connection network. It has a good effect on the control of nonlinear
systems and systems that are difficult to model. In general, neural networks
are used in control systems in two ways: one is to use them for modeling,
mainly using neural networks to arbitrarily approximate any continuous
function and the advantages of its learning algorithm, there are two
feedforward neural networks and recurrent neural networks One type; the other
is used directly as a controller.

The vehicle’s longitudinal dynamics are handled via a pre-tuned PID
controller, whereas the lateral dynamics are handled using an end-to-end
trained neural network. The main advantages of deploying imitation learning-
based control in autonomous vehicles are its straightforward implementation in
addition to its quick and fair training capabilities, i.e., in comparison with
reinforcement learning. Furthermore, the neural network facilitates the
driving learning process for vehicles based on a provided dataset. Although
this can be accomplished without complex formulations governing the system’s
input-output relation, biased and/or improperly labeled data may lead to
incorrect learning by the neural network. Therefore, advancements are still
needed in order to attain reliable performance and accuracy when adopting such
an imitation learning-based control for autonomous driving [82].

###  V-C Deep Learning Control

The research of Neural Networks, meaning deep neural networks (DNN), aka
artificial neural networks (ANN), results in profound thinking [84]. This
model’s strength is presented in the ability to automatically select features
of high-dimensionality data, which helps avoid complex feature manual
selections. The most important feature of using deep learning in such fields
is handling feature extraction techniques within the model (without using
separate data preprocessing techniques to detect features) and fitting a
model. For control systems with high-dimensional data, introducing deep
learning has a lot of significance. Lately, some examinations have applied
deep learning approaches to control systems, such as deep belief network (DBM)
and Stacked Automatic Coding (SAE); CNN [85]; and Recurrent Neural Networks
(RNN).

###  V-D Reinforcement Learning Control

Unlike imitation learning-based control (neural network methods), where the
learner that is trained to mimic the trainer’s behavior may never outperform
the trainer’s performance capability, the reinforcement learning method learns
through trial and error. The “agent” trained using reinforcement learning
discovers the surrounding environment and independently explores new control
strategies. Specifically, $\pi$, the policy to be trained for optimal control
can be a neural network that behaves as a function approximator controlling
the relation between actions (outputs) a and observations (inputs),
$\pi(a|o)$. The agent is rewarded upon carrying out valid actions while
penalized for any improper actions, and thus the goal of the agent, given a
specific policy, is to maximize the expected reward per
$\operatornamewithlimits{argmax}{E}[R|\pi(a|o)]$. However, defining the
expected reward is still highly critical as rewarding the agent for carrying
out good actions may enable them to randomly wander pursuing the reward
maximization (i.e., risk of never accomplishing the training objective) [82].

Last, a summary of comparisons between the traditional and intelligent control
methods is provided in Table IV. The potential integration of AI into control
models (fuzzy logic, evolutionary algorithms, and ML) with a summary of
optimization applications for driving comfort enhancement is further depicted
in Figure 12.

TABLE IV: Traditional vs. Modern Control Methods. Control |  Method |  Pros |  Cons |  Function
---|---|---|---|---
Traditional |  PID
Fuzzy
Optimal
Sliding mode |  Widely used; Increases system performance and simulation controlling output is easy to achieve (i.e., Fuzzy); Better robustness, adaptability and fault tolerance (i.e., Fuzzy) |  Designed for mostly linear systems (i.e., PID), which do not change complex characteristics over time |  Focuses on the study of the conditions and methods to optimize the control system indicators (i.e., Optimal), stability, and comfort (e.g., steering); The controller switches constantly in a hopping fashion according to the current system condition (i.e., sliding mode)
Intelligent |  Model-based
Neural Network
Deep learning |  Control systems with high-dimensional data; Efficient feature extraction and model fitting; The ability to learn by continuously correcting the connection weights between neurons and storing them discretely in the connection network; Provides a good effect on the control of nonlinear systems and systems that are difficult to model |  Boundary detection; Semantic segmentation; High computation power; Requirement of a large amount of training data; Instability under adversarial perturbations |  The feature selection process is done automatically, which significantly expedites the process, especially when dealing with big and high-dimensional data; Enables control process models and robust machine processing implementations for an enhanced driving experience; Enhance driving experience/comfort based on driver’s behavior prediction

###  V-E Trajectory Optimization

####  V-E1 Linear Quadratic Path Planning

In traditional driving, if the driver feels uncomfortable, he can easily
adjust the driving style to improve the comfort in driving and thus the
driving experience. This role was passed to the car operating system after the
vehicle implemented autonomous functions. A system for determining a driving
efficiency is required in order to achieve safe riding in autonomous vehicles.

Considering the importance of ride comfort measurement during the development
and adjustment of autonomous vehicles, the Svensson team designed a system of
adjustment strategies including frequency analysis [86]. The system evaluates
YSIS and ride quality by simulating the lateral control system. Therefore,
they updated the controller configuration and suggested H as the frequencies
weighted linear quadratic controller in order to further boost the conduction
efficiency output through the route planning algorithm. By measuring and
testing their frequency range, the frequency quality of the acceleration data
is calculated. The system chooses to use the power spectral density (PSD).

The system is a lateral control system to change the lateral route preparation
for self-supporting vehicles, taking into consideration ride quality
efficiency [86]. This works by developing a way of determining the right set
of tuning parameters by looking at the balance between reaction and riding
standard. These sums can be measured by graphical instruments [86].

####  V-E2 Path Planning and Optimization

The method based on trajectory optimization is a widely used method in path
planning [87, 88], which represents route planning as a question of
optimization, taking into account the appropriate vehicle efficiency and
related restrictions [89]. Its main advantage is that it can flexibly and
easily encode various requirements on the required path of the AV. At the same
time, advances in solvers for online optimization (CVX [90] and Ipopt) have
helped to provide real-time, fast, and reliable path generation. Owing to the
normal complex and unpredictable driving conditions (which can not be
completely modeled), the model predictive control (MPC) approach is also used
for online AV track preparation [91, 92, 93]. MPC solves a variety of common
problems in the optimization of finite times and during the planning process
can change the environmental status.

The Liu team suggested an MPC-based path plan system, which tackles multiple
modes in a single sense [94]. They do not use preset rules to determine some
operations, but rather to resolve online non-linear MPC issues. The paths
created by MPC are automatically determined by operations including lane
shifts, lane-keeping habits, ramp merges, and intersections. They have chosen
the relaxed convex method for determining lane change and line-keeping
activities in the objective function of MPC [94]. Around the same time, cars
were designed as polygons in order to ensure driving safety. A form of
restriction has been created to implement avoidance of collisions between auto
and surrounding cars. They also have a lane-related area that is designed for
the easy and efficient handling of nearby vehicles in the same lane [94].

###  V-F Comfort Optimization Techniques in AVs

####  V-F1 Overall Comfort Optimization

Comfort is principally affected by the following two factors: Jerk, which
refers to the acceleration shift rate; and Bending Rate, which refers to the
change in curvature. Higher comfort means a smaller rate of change in
acceleration and a smaller rate of change in curvature. The corresponding cost
function can be set as “acceleration + acceleration change rate + curvature +
curvature change rate”. Therefore, the optimization of comfort is mainly
related to horizontal optimization, vertical optimization, and midline issues.

####  V-F2 Response Time Optimization

The reaction time is a system response time, which involves time for cloud
network monitoring and the analysis of laboratory meetings and the system
measurement, and the breaking time for the car. The system’s overall response
time can not be greater than 10 milliseconds if the braking distance of 100 km
is not to surpass 30 cm, and the individual reaction speed of the strongest F1
conductor is around 100 milliseconds.

Thousands of self-directed sensors will increase their speed and intelligence.
Such devices produce unimaginable data sizes well in advance of any other IoT
device. If data needs to be interpreted and analyzed faster than in current 4G
systems, self-driving vehicle systems require exceptional data processing and
speed to simulate time for human reaction. 5G will speed up the network and
reduce the latency of self-propelled vehicles in order to achieve faster
response times.

Leading semi-producers worldwide, including Intel and Qualcomm are going for
the ASIC revolution to tackle the difficult question of integrating 5G
bandwidth with wireless radio and antenna architecture [95]. Briefly, these
firms create chips to transform autonomous cars into mobile data centers,
allowing cars to make complex decisions in real-time.

In terms of supercomputing power, along with the continuous improvement of
cloud computing and on-board computer computing power, the on-board computer
system can process more complex tasks in a shorter time and realize automatic
driving real-time perception of road conditions, intelligent decision-making,
and control. With the emergence and wide application of excellent algorithms
such as ML and DL, AI has entered a new stage of rapid development after 2013.
Applied to the field of automatic driving. The error rate of vehicle
recognition using deep learning methods is lower than that of traditional
methods adopted in the year.

Low end-to-end latency is more dependent on improvements in sensors,
processors, algorithms, and machine transmission. After the deployment of
ultra-low latency 5G networks, we believe that more advanced communication
technologies will bring more innovations to car companies and make AVs more
secure.

The real innovation of the new system lies in its comprehensive signal
processing capability. This allows all processing to be performed directly
within the module, and the system selectively filters the data from the radar
system and the stereo camera so that the processing can be performed
immediately or deliberately delayed to subsequent processing stages. Identify
irrelevant information, but do not forward it. Sensor fusion is applied to
data fusion between the camera and radar. Then, the neural network evaluates
the data and determines the actual traffic impact based on machine learning
techniques. Therefore, the system does not need to send status information to
the vehicle, but only needs to send a response command. This frees the
vehicle’s bus to process important signals, such as detecting a child suddenly
running on the road.

####  V-F3 Motion Sickness Optimization

To optimize the problem of motion sickness in autonomous driving, researchers
have made a lot of efforts, summarized as follows.

##### Anti motion sickness glasses frame

French automaker Citroen has designed an anti-motion sickness eyeglass frame
called SEETROEN [96]. In this solution, there are blue liquids inside the four
rings. These liquids are located on the “visual edge” of the wearer. When the
vehicle turns, brakes, or bumps, the blue liquid will also flow with it, so
that the visual information is obtained by the eyes. Consistent with the
movement signals felt by the cochlea, keeping the brain’s perception
consistent. It is understood that SEETROEN can alleviate motion sickness
within 10 minutes, and the success rate is as high as 95%.

##### Trick the brain

One strategy is to trick the passengers’ brains into thinking they see the
movement of the car, even if they do not. The University of Michigan
Transportation Research Institute has patented a system that simulates visual
cues that people get when viewing from outside a car [97]. This technology
mimics the vestibular input corresponding to the vehicle’s speed,
acceleration, yaw, and lateral motion. Its goal is to blind your sight to the
vestibular system. This allows passengers to focus on other things, which has
been a big selling point for autonomous vehicle technology since its
inception, without the effects of motion sickness.

##### Smooth driving and motion sickness warning system

Google’s Waymo has filed a patent describing a system that eliminates motion
sickness by identifying a route that minimizes motion sickness [98]. Waymo’s
patent solution to motion sickness is to calculate the motion sickness index
of each route, and finally select the route with the smallest motion sickness
index. And communicate motion sickness information with passengers through the
display panel.

The system will suggest providing less complicated driving roads to prevent
motion sickness, or send passengers an alert on bumpy roads prone to motion
sickness, and recommend that passengers do not look down or read books during
the journey [98]. There are also special seats in the car for people who are
prone to motion sickness, and the algorithm evaluates the congestion of
alternative paths in advance to determine the motion sickness index. Waymo’s
technology will study the car’s back-and-forth acceleration and sway to
calculate whether motion sickness may occur on a particular route. When
passengers specify that they feel unwell, the system can also change their
driving style or route. In addition, Waymo unmanned vehicles can adjust
driving styles based on passenger motion sickness reactions, such as
increasing the distance from the car in front.

##### Sensory simulation system

Uber plans to use vibration and mobile seats, airflow targeted at the face or
other parts of the body, and light bars and screens to prevent passengers from
feeling uncomfortable. It applied for a “sensory stimulation system” that is
essentially a vestibular system that trains passengers so that what they see
is related to their feelings [99]. One mechanism proposed for this includes a
light bar that surrounds a portion of the interior and provides visual cues to
alert passengers to acceleration, braking, or changes in direction. It may use
color and brightness levels to indicate different actions.

Uber not only attempts to unify visual and cochlear information, but also
provides a set of immersive experiences including vision, hearing, seating,
and airflow. You might also hear the breeze rushing through the emergency stop
as the vehicle brakes abruptly [99]. First, visually, Uber’s car has a set of
AR (augmented reality, overlaying virtual information in the real environment)
system, which can use the environmental information outside the car captured
in the car to present in the car, making up for the visually immobile
perception Dislocation to synchronize somatosensory information. A matching
tactile system is also provided on the seat, which will give the corresponding
airflow when turning sharply and rapidly.

##### VR games

Holoride is trying to eliminate motion sickness with VR glasses [100]. The
movement in VR games is consistent with the vehicle trajectory so that the
movement we see is consistent with the movement felt by the body.

##### Vestibular electrical stimulation

Most of the above methods change the visual information so that the visual
information is consistent. vMocion attempts to alter vestibular information to
align it with visual information [101]. vMocion’s 3vTM platform is a device
based on electrical vestibular stimulation (GVS), which can potentially be
used to convert motion feedback to users [102]. By placing 4 electrodes in the
ears, neck, and forehead, electrical stimulation is used to align the movement
detected by the nervous system with the visual content. Figure 13 shows
vMocion’s 3vTM platform.

Figure 13: vMocion’s 3vTM Platform based on GVS to convert motion feedback to
users using 4 electrodes in the ears, neck, and forehead [102].

Samsung has already created an Entrim 4D headset built on the GVS device. The
team sees Entrim 4D as a potential response to the movement disorder issue
[103]. However, the technology is still under development.

####  V-F4 Lane Change Maneuver and Optimization

The required speed can be regulated according to the route radius and a quick
transformation can be achieved [104], This eliminates the disparity between
vision and human vestibular system in fluid travel, making the journey
smoother. Smooth motoring techniques will boost vehicle satisfaction and
closely track accelerating, braking and steering. The quick lane changes or
curves generate a vehicle’s lateral acceleration, so developing new sufficient
lane change maneuver algorithms helps improve AV comfort.

There are different techniques for modifying the routes of driverless
vehicles, with Bezier curves the most widely used way to create routes for
lightweight communication and marshaling (LCM). A Bezier curve-based route
planning algorithm has been proposed to create a reference path to meet the
roadside restrictions in [105]. Also, a Bezier curve-based collision-
prevention technique was introduced by [106] that considers all path lengths
between automobiles. Chen et al. considered the incomplete constraints and yaw
rate parameters of vehicle steering and developed a Bezier-based lane change
algorithm [107]. In another work, Kawabata et al. suggested to Bezier a
3-dimensional refer to a Curve generation constraint optimization method
[108]. Moreover, an efficient LCM Bezier curve-based algorithm was developed
[109] to estimate potential lateral acceleration to be employed in a local
road planning phase to transfer the side motion of the vehicle during lane
change maneuvers smoothly. The algorithm measures the curvature of the Bezier
curve by the parameters of the lateral acceleration factor reached by the
operator in order to restrict lateral motion. If lane change maneuvers are
required, space availability must first be confirmed to generate a local path.
The planning block then produces alternative pathways and tests the local
pathways generated to determine whether they fulfill a certain acceleration
limit.

##  VI Applications of Driving Control Methods and Comfort Optimization
Techniques for AVs

This section covers some applications of driving control methods and comfort
optimization strategies for AVs in various scenarios. Interestingly, a couple
of these works incorporate path-planning mechanisms, as seen in [110] and
[111].

###  VI-A Speed Control Framework for AVs - Rough Pavements

A deep reinforcement learning-based speed control strategy has been proposed
for AVs on rigid pavements [112]. The developed approach aims to estimate
riding comfort and energy-efficient speed control. The proposed framework
utilized open-access real-world data for training the model. The model
achieved energy and computation efficiencies by 8%, 24%, and 94%, for which
the authors claimed their model would be a good choice for controlling AVs
speed on suburban roads.

###  VI-B SAINT-ACC

An adaptive deep reinforcement learning-based cruise control system called
Safety Aware Intelligent ACC (SAINT-ACC) has been proposed to optimize the
efficiency of traffic, safety, and comfort [113]. SAINT-ACC can perform
driving safety assessments via modification of safety model parameters in
response to various traffic conditions. Moreover, the proposed approach also
utilized a dual reinforcement learning system, improving traffic efficiency,
safety, and comfort. A separate reinforcement learning component is designed
to find a specific threshold based on traffic data obtained from the
environment. This model interacts with another reinforcement learning-based
model to identify the insufficiency in the riding flow, safety, and comfort.
The two reinforcement learning agents are trained via numerous highway
scenarios, including those with on-ramp and off-ramp highways. A simulation
platform is also used to perform experiments over 12,000 runs for training.
Results obtained from these tests confirmed SAINT-ACC’s ability to enhance
driving safety, traffic flow, and comfort.

###  VI-C Real-time NMPC Path Tracker

A non-linear predictive-based path tracker model for AVs has been proposed
[114] using C++ and several powerful libraries to guarantee optimal real-time
performance. The listed work claimed it featured by high flexibility in a way
that allows the incorporation of multiple objective terms to form a cost
function. The proposed model aims to provide precise tracking while
maintaining a comfortable lift. After testing the proposed approach on
numerous simulation studies that featured intricate tracks with many sharp
turns, the authors confirmed that the proposed path tracker could outperform a
regular PID-based controller of an AV.

###  VI-D TDR-OBCA

An optimized collision avoidance model for AVs driving has been presented in
[115] to drive comfortably and efficiently. The TRD-OBCA is designed to
provide a smooth collision-free trajectory model for AVs in accessible
environments, such as parking lots or off-road areas. The TDR-OBCA algorithm
has been tested and validated in simulations and real-world road experiments.
Valet parking and various obstacles have been illustrated in simulations. This
approach claimed to drastically reduce the loss by 97%, while increasing the
computation time by 45% and improving the overall driving comfort scores. The
proposed model is claimed to lower the steering control output by more than
13.5%, for which the authors suggested using it for real-world AV
applications.

###  VI-E Model Predictive Control

The purpose of [116] was to propose a method for designing a model predictive
control scheme to enhance the passenger’s comfort for AVs. According to the
authors, the proposed strategy is different from previous attempts because of
its wide applicability in various driving scenarios and incorporates an
offline method weighting parameters of the model predictive control scheme.
The authors tested the proposed approach with experiments conducted in
Matlab/Simulink and the tests were done using the highway and urban setting
scenarios. The proposed approach utilized the MSDV and acceleration
coefficient metrics to evaluate the model performance to meet the ISO 2631
standard requirements. The system also considers the ride comfort and drive
quality described by ISO 5805 standards. Ride comfort is identified as “a
subjective state of welling and absence of mechanical disturbance concerning
the environment and ride quality.”

##  VII Implications, Insights, and Future Works

###  VII-A Comfort and Health Risks

AVs may strengthen the disadvantaged position of humans in the battle against
pollution and sedentary behavior, affecting the health of drivers and
increasing health risks. Automated driving reduces traffic pressure and the
apparent danger of car travel, which leads people to rely more on vehicles.
Even for short distances, people may choose to drive by car instead of
walking.

Sedentary motion and frequent exposure to pollution are the two most common
risk factors for heart disease. Statistics show that about one-quarter of
Americans die of heart disease each year. People who drive frequently often
live a sedentary life, and their incidence of obesity, diabetes, and heart
disease will also increase.

In the 20th century, more and more people owned their own cars, which allowed
them to work in the city without having to live in the city, which means that
the core of the city is surrounded by a growing number of suburban residents.
Therefore, for many people, walking is no longer an option. The growth of the
city also presents significant problems for public transport: the need for
buses or subways with longer kilometers is growing as the number of residents
grows. This situation requires everyone to make a choice, either buy a car or
bow to reality and choose a longer commute.

A future full of self-driving cars means less walking time and more urban
expansion for people, and if the development and progress of electric vehicles
cannot keep up with the requirements of the times, then pollution is bound to
increase sharply. After all, when people drive self-driving cars and can do
anything they want to do on the commute, the driver doesn’t care whether the
time on the road is 20 minutes or an hour.

###  VII-B Benefits and Costs of Driving Comfort Adoptions

####  VII-B1 Practices in Designing Safety and Comfort of AVs

The vehicle must not completely rely on the GPS function. The vehicle needs to
cross-reference the pre-drawn map and real-time sensor data, and after
comparing the two, determine the location of the vehicle. A notable example of
such a design practice is the Google Waymo use case. The Waymo team aims to
develop a comprehensive high-resolution 3D map before driving, which will show
road lines, curbs and sidewalks, streets, crosswalks, stop signs and other
road conditions [117]. Furthermore, the self-driving car sensors and software
must continuously scan the surrounding environmental information within 300
meters and read traffic control information from the color of traffic lights
and railway crossings Temporary Stop Sign. To enhance driving comfort levels,
the deployed algorithms will forecast the potential travel on the basis of the
present speed and direction of various complex path goals, and can even
distinguish the difference between vehicles and cyclists or pedestrians. The
software needs to use information obtained from other road users to anticipate
a variety of possible driving routes and predict how the road surface will
affect other targets around the vehicle.

####  VII-B2 Benefits

Due to the reduced time, energy, and money required to travel, GTC is expected
to decrease. The comfortable travel, first of all, the safety of travel,
reliability of travel time and other activities than driving like work,
meetings, food or sleep, etc. are more important [118].

The effect of self-employed cars on the time demand of travelers has been
minimal. A preference survey conducted by Yap, Correia and van Arem in the
Netherlands found that the use of fully automatic (level 5) has a higher time
value than the use of manually driven vehicles as a train travel exit mode
[119]. Such researchers offer the impression that driving a driving vehicle
will offer passengers a feeling of uneasiness, lack real-life knowledge with
self-drift vehicles, and outbound trips are typically short trips and
travelers do not have the ability to completely benefit from self-driving
vehicles, including protection in their journey. Cyganski team suggested that
in the questionnaire survey in Germany, only a very small number of
respondents referred to the working ability (level 3 and higher) of autonomous
vehicles as an advantage [120]. In comparison, most respondents accepted that
behaviors, such as looking, chatting, or enjoying music, should also be
relevant while traveling in a self-employed vehicle which they usually
practiced when driving a regular car. Answers who have been found working in
the current route prefer to work in independent vehicles.

The free passage capability, distribution of vehicles along the roads, and
consistency of traffic can be advantageous for individual vehicles [118].
Higher freeflow levels and reduced traffic levels will improve road capacity
and thus decrease congestion time, which is the reduction in the amount of
queued pollutant emissions. Quoted congestion delays are minimized as a result
of improved road availability and minimized (or even eliminated) duration of
parking search due to car parking space. The use of modern cars has also
expanded and can take less time to drive.

In their study, Hoogendoorn et al. concluded that self-driving would decrease
traffic congestion by 50 percent, and this degree of reduction can be improved
with the help of technology in cars and buses [121]. The Michael team revealed
that, although the degree of coordination between cars is growing, efficiency
is being seen in the simulation of a single-wave automatic highway network
[122].

Autonomous vehicles should be implemented to facilitate the growth of motorway
and car-sharing systems. The running costs for ride-sharing and car rental
systems will be greatly lowered for autonomous vehicles. Autonomous cars can
fulfill individual travel criteria easily at lower costs and greater
versatility [118]. Then city residents may agree to limit or even eliminate
the number of cars they own. In the hypothetical and real town of Zurich,
Zhang et al showed simulated in Switzerland that about ten and fourteen
traditional cars can be replaced by each shared self-driving car [123]. The
substitution rates for private vehicles, according to Chen, Kockelman, and
Hanna, are between 3.7 and 6.8 if automotive charging is not used in the
public, hybrid and self-driving cars [124].

Regional and local area preparation can be influenced by autonomous vehicles.
People can choose to use self-driving shared vehicles instead of driving
themselves, so parking areas can be reduced, and the increased space can be
used to build more suitable public facilities for life. Reducing parking
demand can also bring about changes in land use and architectural design.

In the background of the urban economy, Zahalenko studied the effect of fully
autonomous vehicles [125]. The researcher has developed a semi-circular
single-center two-dimensional city model that has been calibrated to
representative cities in the United States. He believes that workers can
choose between not commuting, traditional commuting, and automatic commuting,
taking into account the variability of each choice, parking fee, and fixed
charges. Statistics suggest that about 97 percent of daily car parking needs
will be moved beyond the city center to “dedicated parking areas.” In
addition, the economic growth in the city center will have a favorable impact
and would raise land prices by 34%. On the other hand, the decreased travel
costs arising from the use of electric vehicles will lead to urban growth, and
land rent will decline by about 40% from areas beyond the city center.

Due to the lower risk of accidents and the higher fuel efficiency of vehicles,
the efficiency of traffic flow is improved and the monetary cost of travel can
also be reduced. The limited distance allows air resistance to be minimized
and therefore the consumption of fuel and costs to be decreased. The
implementation of auto drives will also offer incentives for electricity and
pollution.

Wu et al. showed a fuel efficiency optimization program that can suggest the
right acceleration/deceleration values for the driver or automatic device,
considering vehicle velocity and acceleration, as well as existing speed
limit, size, road lighting, and road sign [126]. Their studies with driving
simulators in urban environments of signalized intersections have demonstrated
a 31% drop in drivers’ fuel consumption.

####  VII-B3 Costs

It is expected that autonomous driving incurs additional costs where the AV
cost (e.g., vehicle equipment development, equipment maintenance, etc.) is
regarded as the main constraint to achieving the maximum level of benefits.
The overall AV cost comprises manufacturing cost, operating cost, and social
cost.

##### Manufacturing cost

  * •

R&D: Needless to say, R&D is the core of autonomous driving. Because it is
difficult to achieve the desired output in recent years, the unit R&D cost
will be high. If a self-driving R&D team has 500 people and the average annual
salary is 200,000 US dollars, the total salary the company pays to employees
every year is 100 Million. If this company has an amazing production capacity,
it can build 5,000 self-driving cars every year, and the unit R&D cost is $
20,000.

  * •

Sensor: Sensors are the most important part of the cost of autonomous vehicles
[127]. Taking General Cruise as an example, in November 2017, a Cruise will be
equipped with 14 cameras, 5 LiDARs, and 21 radars. Moreover, the sensor
technology is updated very quickly, and the LiDAR introduced at the beginning
of the year may become obsolete by the end of the year. So even if the cost of
the sensor itself is getting lower and lower, the cost of replacement will not
be reduced.

  * •

Assembly and mould: The sensors of self-driving cars are many and complicated,
and the assembly process will not be simpler than traditional cars. A set of
molds for traditional cars can reach 200 Million US dollars, if it is a new
design it will reach 600 Million US dollars. For self-driving cars, if the
body is a new design, and the replacement speed is fast, the mold cost will
remain high.

  * •

Materials: Most self-driving car R&D companies have begun to use energy-saving
and environmentally friendly materials to manufacture car bodies to ensure
that the car body is light and energy-saving [127]. Waymo uses plastic as the
windshield and fiber material as the seat. There are also cushioning materials
in the front and rear to reduce the impact of the impact. Some materials will
be cheaper than the metal plates of traditional cars.

  * •

Interior: Although the interior of a self-driving car will add a lot of
experience products, such as in-car screens, in-car sensors, desks, stereos,
etc., but because many traditional car functions will be transferred to the
mobile phone APP, The overall cost may still be cheaper than ordinary cars.

##### Operating cost

Maintenance should be the largest cost in autonomous driving operations. A
self-driving car repairers are not as easy to recruit as regular car
repairers. Not only must they be rigorously trained, but they also need to
have software engineers to assist them in maintenance [128]. Various parts and
batteries also need to be replaced from time to time. However, because each
driving behavior of an autonomous vehicle can be strictly calculated, it will
not step on the accelerator or brake like a human driver, and it can also
avoid hard-to-open road sections (such as climbing), and the maintenance of
parts is easier.

##### Social cost

Big data is the subject of many sectors and is no different for the automotive
industry. Nevertheless, the automotive industry will become a collector of
data, and a big database developer with the introduction of self-driving
vehicles. An autonomous vehicle can generate 100GB of data per second [129].
Self-driving cars depend on huge quantities of data transmitted by various
sensors installed. Cars that drive will know exactly where they are and where
they are going to track something that is encountered when driving [130]. The
world in which the vehicle is situated and those that use it must always be
recognized by self-drive vehicles. The better an auto is, the more useful it
is for car consumers. However, cars need more and more personal data to make
them smart and incorporate the data results into the services provided.

Users are paying more attention to the threat of AVs to their privacy. Self-
driving vehicles are likely to compound this problem. Consumers and regulatory
authorities are conscious that vast amounts of personal data and information
about consumers and the environment will be produced, used, and preserved in
these vehicles. Automobiles are real data factories. It means that anonymity
is compromised in the world mobile surveillance ensures that. From the
perspective of protecting privacy, the potential data that self-driving cars
can collect and how these data are used have received increasing attention.

A travel mode is one of the most important results. Automobiles provide
historical geographical data and continuous geographical data in real-time.
Such details would not only help a third party to know where the user is and
where the user is now, but it will also know where the user is. Announcers
should be able to map user-frequented shops to recognize customer purchasing
habits. Insurance undertakings may recognize particular personality types
[131] by tracking users’ daily activities or eating habits, such as frequent
fast food restaurants or gyms.

The constant information gathering of cars is a cause for worrying that
personal information is used for the purpose of targeted ads and
advertisements, and drivers may be bored or even dangerous to others. The
auto-driving sensors continuously monitor the surroundings and collect
photographs would result in potential privacy breaches. The users of auto-
driven vehicles are likely to worry about gathering or using their personal
details without their consent, intent, or effect on users themselves.

Self-moving vehicles gather and show information about where, when, and how
people travel from one location to another, basically automatically. Users are
concerned about the reason for which such personal information is to be used.
How do we gather this information? What is the purpose of the data? How long
is the data maintained? Who is able to access the information? Some analysts
suggest that the user adoption of autonomous vehicles would be influenced
significantly by safety issues. Anderson et al. [132] believe that autonomous
driving will lead to additional costs such as more travel, destruction of
parking income and loss of work. Litman [133] has summed up the cost to
vehicles and infrastructure of autonomous driving, threatening, anonymity,
increased transport, social equity, and lower employment. A summary of AV
passenger’s benefits and cost are provided in Table V.

TABLE V: Summary of Passenger Benefit (B) and Cost (C) Effect | Passenger
---|---
Saved time because the driver would do something else in the car | B
Saved fuel because of faster driving | B
Decreased emissions from forests due to reduced fuel consumption | B
Improved safety in traffic | B
Enhanced travel due to lower operating costs | B
Developing equipment for cars | C
Superior maintenance expenses | C
Introduced change posts as the travel markets do not apply fixed social risk rates to customers | B/C
Modified charges for AVs repair | B/C
Seen improvements in AV security and privacy | B/C
Infrastructure maintenance prices have increased | B/C
Changed land use | B/C
Changed congestion | B/C

###  VII-C Future Research Directions

Figure 14: Taxonomy of potential future research directions in AV comfort.

Because the technology is not yet mature, and the mass production of
autonomous vehicles will take time, autonomous driving still has a long way to
go before landing at the L4 and L5 levels. Autonomous driving must confront
various problems if it is to be widely used. In addition to advanced sensor
technology, more sensor fusion technology is needed. There are still many
technical obstacles to be solved. Secondly, there is no doubt about
regulation. The surrounding environment in which autonomous vehicles can be
used should be clearly specified. In addition, consumers’ familiarity and
acceptance of autonomous driving solutions is also a point that cannot be
ignored. Autonomous driving must allow people to safely give control of
driving to a set of very intelligent processors, sensors and software systems
behind it. The arrival of driverless driving may take some time, but there
will always be a day when it will become popular. In this case, it is not just
the type and way people buy cars, but also how they own cars. This section
discusses potential directions for future research work focusing on the
comfort aspects of AV users. A taxonomy of these directions is depicted in
Figure 14 and discussed in detail next.

####  VII-C1 Motion Sickness Reduction

Although motion sickness is an unavoidable problem for autonomous driving
(i.e., about 40% of people have serious symptoms of motion sickness due to
severe discomfort), comfortable autonomous driving can support our various
activities in the car and reduce motion sickness severity. A future
improvement direction can be about addressing the car’s stimuli making the
vestibule to the center and sense which directly contributes to motion
sickness problems. The stimuli here are mainly generated when the vehicle
undergoes a linear acceleration (deceleration) speed change, and acceleration
and deceleration braking.

The overall comfort of autonomous driving can be enhanced through jerks and
accelerations. This can also improve the AV user’s experience and enable
sustainable driverless services. Therefore, more research work is still needed
to investigate the comfort levels in AVs from the perspective of the different
jerk and acceleration factors in order to provide proper jerk and acceleration
criteria for planning and self-driving strategies. Furthermore, it is highly
recommended that future planning and controller solutions for AVs are verified
and validated over real-time software environments in accordance with
different acceleration criteria.

Last, proposed solutions for motion sickness optimization must be validated
based upon a comparison analysis between autonomous driving and how the human
driver operates in terms of the driving comfort experienced by both drivers
and passengers.

####  VII-C2 Enhancement of Non-driving Tasks Experience

As AV comfort also concerns both the drivers and passengers, non-driving
related jobs must be considered and tested in accordance with safety and
comfort design practices. These non-driving activities or tasks can be
challenging when designing a universal prototype to improve the users’
situation awareness. Hence, further automotive research works may focus on
specific non-driving tasks inside the AV to maximize the overall comfort
experience of the drivers and passengers.

####  VII-C3 Modalities of Peripheral Information Display

To improve the situation awareness of AV users, further research work may
consider testing a broad range of modalities to convey peripheral information
in the vehicle. Up to date, research works have focused on a single modality
of peripheral information display such as the haptic cue. Such a single
modality can improve situation awareness, but however, it cannot mitigate
motion sickness. Therefore, future research direction could be the exploration
and integration of various modalities to enhance the overall awareness of AV
users.

####  VII-C4 Minimizing Involuntary Movement

To enhance the AV users’ experience and comfort, active mechanisms for
movements can be considered for the optimization of AV users’ involuntary
movement. This can help avoid or reduce the prolonged postural instability
that leads to the increase of motion sickness over time. Specifically,
reducing or eliminating the involuntary movement of AV users by countering the
impacts of centrifugal force upon turning into corners is a potential research
direction in improving the overall AV comfort level. This direction will
require collaborations between human factors experts and automotive designers
and engineers.

####  VII-C5 Visual Capabilities and Road Conditions Adaptation

Improving visual ability of the car is also a challenge faced by current
driverless cars. The driverless car needs to recognize other vehicles in the
surroundings and must also be able to detect the surrounding lanes,
pedestrians, traffic signs, etc. in various challenging environments (such as
rain and snow). In addition, challenging road conditions are another problem
that driverless cars need to consider and solve.

####  VII-C6 AI and Driving Comfort

The simulation of human intelligence via AI is drastically influencing the
growing number of AVs and the sophisticated services they offer. Over 90% of
all car accidents are typically due to driver errors (e.g., poor reaction to
the violation or a hazard of traffic laws, poor anticipation, etc.). Hence,
further integration of AI techniques in future autonomous driving technology
can help with the stagnation in the road accidents rate decline, while
increasing the comfort and trust of users in AVs.

Furthermore, governments and legislative authorities may consider making ADAS
technology mandatory in all future AVs. ADAS technology will have an AI module
to monitor, analyze, and recognize possibly unsafe and dis-comfortable driving
behaviors. Specifically, if comfort-disturbing behaviors, such as
distractions, drowsiness, and lane departure, are detected, the system can
issue a real-time alert warning the driver about the potential danger.

AI-enabled driving control methods can improve the overall driving experience.
For instance, navigation systems leverage a broad range of information (e.g.,
road traffic conditions, real-time weather conditions, etc.) to optimally
guide the driver to their destination with the least hassle. A similar
component can be integrated as a sub-module of the overall intelligent speed
assistance system to support fuel efficiency by providing suggestions about
the ideal moments to brake/accelerate to the driver [66], [134].

####  VII-C7 Manufacturer Considerations

When designing future AV, the key distinct areas of safety and comfort that
must be addressed are interpersonal protection, operating safety, crash
safety, and health without a crash. These critical factors are summarized as
follows.

  * •

Comportment protection requires driving decisions and actions on the lane. AVs
also need to abide by traffic rules, and must provide navigation for users to
ensure driving safety in various expected and unexpected scenarios. This
problem can be formulated based on precise safety and comfort criteria, where
a multi-pronged test over various methods and drive assays must be used.

  * •

Functional safety helps to guarantee security tests in AVs in the case of a
device malfunction and a backup framework for adapting to the vehicle’s
unexpected circumstances.

  * •

Collision tolerance refers to the driver’s ability to shield occupants in the
car by different steps, shielding people in the driver by structural
construction, including seat belts and airbags, and minimizing injuries.

  * •

Operational security refers to the interaction between the vehicle and
passengers (i.e., HMI). When the safety of operation is ensured, AV can ensure
that consumers can bring the comfortable experience provided by AVs. The
company will consider enhancing the users’ comfort by integrating an interface
that allows them to understand their destination, direct vehicles to park by
the side, and contact the car’s manufacturer to obtain technology auxiliary.

  * •

Non-collision safety for people who may interact with vehicles. The AVs’
manufacturers must aim to provide physical protection against hazards caused
by electronic systems or sensors.

####  VII-C8 Efficient Comfort Level Analysis

The AV companies must consider employing different methods for safety and
risky comfort levels analysis. The risk analysis methods will help define the
necessary specifications for autonomous driving system design, subsystems, and
components. The designated safety and comfort standards need to follow a set
of methods for device and system review, different procedures for systems
engineering construction, and the specifications of international and national
legislation.

##  VIII Conclusions

With the promotion of artificial intelligence (AI) technology, self-driving
cars are showing fast-paced development, mainly reflected in environmental
awareness, decision-making and planning, control and execution, high-precision
maps, and real-time positioning technologies. This article explains the
comfort of autonomous driving from various perspectives. With that being said,
comfort and its associated factors are critical indicators in self-driving
cars. Although comfort levels vary from one user to another, they are impacted
by many factors, such as the reaction time of autonomous vehicles (AVs), which
impacts the comfort of passengers both physiologically and psychologically.
Furthermore, when it comes to the planning and design of self-driving cars, as
far as the performance of self-driving cars and their user comfort are
concerned, user comfort is often ignored. This is typically due to the lack of
comprehensive investigations on the control mechanisms in regard to self-
driving service operations that relate to user comfort driving factors. This
paper discussed the various possibilities impacting the AV’s user comfort
based on a broad range of technical aspects, biomedical sensors, and
psychological analysis. This paper can serve as a baseline for researchers to
further address the issues impacting the driver’s reaction time in an
incident, the level of comfort of the AV, the movement dysfunction, smoothness
and jerkiness, and whether autonomous driving is relaxed and safe.

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