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Citation: Rahman, M.M.; Thill, J.-C.
What Drives People’s Willingness to
Adopt Autonomous Vehicles? A
Review of Internal and External
Factors. Sustainability 2023,15, 11541.
https://doi.org/10.3390/su151511541
Academic Editor: Juneyoung Park
Received: 2 July 2023
Revised: 20 July 2023
Accepted: 24 July 2023
Published: 26 July 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Review
What Drives People’s Willingness to Adopt Autonomous
Vehicles? A Review of Internal and External Factors
Md. Mokhlesur Rahman 1,2 and Jean-Claude Thill 3 ,*
1The School of Information Studies, Syracuse University, 343 Hinds Hall, Syracuse, NY 13244, USA;
mrahma18@syr.edu or mrahman.buet03@gmail.com
2Department of Urban and Regional Planning, Khulna University of Engineering & Technology,
Khulna 9203, Bangladesh
3
Department of Geography and Earth Sciences & School of Data Science, University of North Carolina at Charlotte,
9201 University City Blvd, Charlotte, NC 28223, USA
*Correspondence: jean-claude.thill@charlotte.edu
Abstract:
This article presents a state-of-the-art literature review to understand people’s perceptions
and opinions of Autonomous Vehicles and the factors that influence their adoption. A strategic
literature search was conducted to select articles for this review. Most of the articles were published
since 2015 and they used a household questionnaire survey to collect data. Mostly, they used
statistical and econometric methods to evaluate the factors that affect people’s intentions to adopt
Autonomous Vehicles. The results show that psychological factors often appear as the most important
internal factors of people’s willingness to adopt Autonomous Vehicles. Additionally, other internal
factors such as the socioeconomic profile of individuals and their household, and knowledge and
familiarity with Autonomous Vehicle technologies would affect adoption tendencies. User attributes
also indirectly affect adoption of Autonomous Vehicles by influencing the psychological factors of
users. We identify several critical external factors such as opportunities (e.g., safety and security,
low congestion, energy use) and challenges (e.g., system failures, privacy breaches, and legal issues),
while another influential group includes transportation factors (e.g., travel mode, distance, and
time), urban form (e.g., urban/rural, density, land use diversity), affinity to new technology, and the
institutional regulatory environment. We discuss some recommendations for policy makers, auto
industries, and private stakeholders to formulate policies and strategies to increase the market share
of Autonomous Vehicles. Finally, we identify some limitations of previous studies and provide a
blueprint for future research on Autonomous Vehicle adoption.
Keywords:
autonomous vehicle; self-driving car; public perceptions; willingness to use; human
factors; new technologies
1. Introduction
In this age dominated by motorization, policy priorities on traffic safety, congestion
mitigation, and carbon emission reduction motivate business and civic leaders to seek
the deployment of alternative mobility options. Recent technologies and services such as
Electric Vehicles (EVs), Connected Vehicles (CVs), Autonomous Vehicles (AVs), and shared
mobility are the most significant advances in modern transportation; they are expected to
transform overall transportation systems in the coming years [
1
–
6
]. These technological
breakthroughs may bring transformational changes in vehicle ownership, travel patterns,
parking demand, infrastructure supply, energy use, emissions, and accidents [
7
–
9
]. How-
ever, as some of them remain to be deployed commercially, such as in the case of AVs,
the extent of the impacts put forth on personal mobility, on vehicular movement, and on
urban development patterns is still quite uncertain and often assessed only with computer
simulations [
10
]. While it is envisioned that people will interact with AVs actively, as
passengers, or passively, as road users [
1
], an assessment of their willingness to accept this
Sustainability 2023,15, 11541. https://doi.org/10.3390/su151511541 https://www.mdpi.com/journal/sustainability
Sustainability 2023,15, 11541 2 of 29
new technology is crucial to predicting trends in the market penetration of AVs [
11
] and to
planning for ensuing degrees of departure from business-as-usual scenarios in urban and
territorial organization. Thus, this study seeks to review and interpret the fast-growing
body of literature aimed at understanding the factors that influence people’s willingness
to adopt AVs, and, by doing so, at understanding people’s perceptions and opinions on
AV functionalities.
In a narrow sense, AVs (also known as self-driving, driverless, and robotic cars) are
vehicles that can drive and navigate themselves without human control by using sensing
technologies (e.g., radar, Global Positioning System (GPS), and computer vision) and control
systems (i.e., sensors) [
8
,
12
,
13
]. More broadly, according to the National Highway Traffic
Safety Administration (NHTSA), AVs are those vehicles in which at least one of the critical
safety control functions (e.g., steering, acceleration/deceleration, or braking) are performed
without human input [
14
]. They have some level of automation to assist drivers or replace
drivers by taking full control of the vehicle [
15
]. The Society of Automotive Engineers
(SAE) differentiates five levels of vehicle autonomy ranges, from Level 0 (no autonomy)
to Level 5 (full autonomy) [
16
]. Level 0 indicates no automation, and the vehicle is fully
controlled by a human driver. In Level 1, the vehicle has some driver assistance system for
either steering or acceleration/deceleration. Partial autonomy is ascribed in Level 2, where
the vehicle has driver assistance systems for both steering and acceleration/deceleration.
In Level 3, the vehicle’s automated driving system has a specific performance, with the
expectation that the driver will respond. Level 4 indicates higher automation of the vehicle;
it has a specific performance by an automated driving system, even if a driver does not
respond. Level 5 indicates the full automation of the vehicle, and the vehicle is operated
by an automated driving system without human intervention. In this paper, we analyze
and discuss factors of adoption at Levels 2 to 5, as the propensity to adopt is a rather fluid
phenomenon and so are human perceptions and opinions vis-à-vis AV functionalities.
Researchers have echoed the expectation that AVs could offer a wide range of social,
economic, and environmental benefits to city dwellers, despite some concerns about system
security and data privacy [
17
]. They have projected a reduction in traffic crashes, congestion,
vehicle ownership, parking demand, energy consumption, and emissions, as well as an
increase in human mobility and convenience [
18
–
20
]. Additionally, Shared Autonomous
Vehicles (SAVs) have the potential to reduce overall travel distance and time by reducing
empty Vehicle Miles Traveled (VMT). Considering the enormous possibilities of AVs as
a new mobility option, governments and manufacturers around the world are showing
a growing interest in formulating AV policies, in adopting AV technology elements, and
in on-road vehicle testing [
21
,
22
]. Most automobile companies have retrofitted existing
vehicles by incorporating some extent of autonomy, and some companies have developed
and tested full AVs. Thus, AVs are not a fantasy anymore, and it is expected that, very
soon (i.e., Level 2 vehicles by 2025, Level 3 by 2040, and Level 4 or 5 by 2050), they will be
used by millions of people for their daily travels. At present, most commercially operated
AVs include only Level 1–Level 3 autonomy (e.g., emergency braking, blind-spot detection,
lane-keeping), due to limited progress in technology and due to the high cost of sensors [
23
].
Researchers have argued that a higher level of vehicle autonomy would induce people
to improve their outlook on adoption [
17
]. Although many studies have investigated
the level of human acceptance of AVs, they often do so inadequately, particularly with
regard to the pace with which this new technology will be accepted and adopted [
23
–
25
].
It is also postulated that intricate regulations, technical difficulties, public perceptions,
and safety concerns will restrain the broad-base adoption of AVs [
26
]. However, public
perceptions of AVs are rather fluid, evolving rapidly with increasing access to vehicles and
more widespread discourse on this mobility technology [25].
Over the past years, a few review studies have sought to synthesize the state of
knowledge on people’s perceptions of AVs and on the factors that influence people’s
intentions to adopt AVs. However, they have seldom discussed the external factors of AVs
(e.g., environment, institutions) [
27
,
28
]; they have overlooked significant psychological
Sustainability 2023,15, 11541 3 of 29
factors such as perceived benefits, perceived behavioral control, and social influence [
29
,
30
].
According to different behavioral theories, such as the Theory of Reasoned Action (TRA),
the Theory of Planned Behavior (TPB), and the Technology Acceptance Model (TAM),
different internal and external factors govern the public acceptance of AVs [
29
]. Thus, it
is necessary to include all factors to capture a holistic understanding of the determinants
of AV adoption. Additionally, people’s perceptions and determinants of AVs have been
changing rapidly due to the evolving nature of this novel technology [
31
]. A recent
article reviewed studies on public acceptance of AVs and investigated the spatio-temporal
distribution of publications and emerging research trends by using bibliometric techniques;
however meritorious this undertaking was, the study disregarded the key factors of AV
adoption [
32
]. Moreover, considering the recent rapid rise in the number of publications
on public perceptions, behavioral intentions, and determinants of AVs, it is imperative
to conduct a comprehensive review study to develop a firm understanding of people’s
perceptions of AVs, people’s behavioral intentions to use AVs, and the key determinants
that influence adoption of AVs. Thus, an updated review study centering on people’s
perceptions and on all factors of AVs is needed in order to understand the current state of
affairs on this subject and to outline a pertinent agenda for future research. The structured
identification of the factors that condition public perception leads to an evidence-based
understanding of the likelihood and pace of AV adoption by the public at large, and it lays
the pathway to the successful integration of AVs with existing traffic management systems
and practices [24]. Therefore, in this review, we will:
(1)
Evaluate the state of perceptions and opinions of people on AV functionality in
different study contexts;
(2)
Identify the internal and external factors that condition people’s proclivity towards
AVs and the functionality they provide; and
(3) Specify research gaps in the existing literature and where opportunities exist for future
research on willingness to adopt AVs.
This state-of-the-art review study makes significant contributions to the literature by
synthesizing existing published works on public perception and determinants of AVs. It
has five specific contributions. First, this study critically evaluates the selected articles in
order to understand people’s perceptions and opinions about AVs. Second, it determines
the key internal and external factors that influence people’s intentions to adopt and use AVs.
Third, a conceptual framework is proposed articulating the external and internal factors
of AV adoption, which will be helpful for policy makers in conceptualizing this complex
ecosystem. Fourth, this study presents some guidelines for transportation planners, policy
makers, auto industries, and concerned private stakeholders to use to formulate appropriate
policy strategies in order to encourage people to use AVs and to increase the market
penetration of AVs. Finally, this paper identifies several shortcomings in the literature and
frames directions for future research.
The steps involved in the overall structure of this review study are illustrated in
Figure 1. In Step 1, we performed a preliminary literature review and conceptualized this
study. On this basis, we finalized and formulated the study aims and objectives in Step 2. In
Step 3, we searched for relevant articles and reports in different databases based on defined
keywords. The articles assembled by the literature search were screened based on some
inclusion criteria and included for this review in Step 4. In Step 5, the included articles
and documents were critically reviewed to determine study contexts, data sources, sample
sizes, methods used, core themes, and key findings. We analyzed the data extracted from
the articles and reports to develop a conceptual framework in order to understand people’s
perceptions and the key factors of AV adoption in Step 6. Results were discussed and
policy recommendations were proposed to encourage people to use AVs and to increase the
market penetration of AVs in Step 7. Finally in Step 8, we identified gaps in the literature
and provided directions for future research.
Sustainability 2023,15, 11541 4 of 29
Sustainability 2023, 15, x FOR PEER REVIEW 4 of 30
in order to understand people’s perceptions and the key factors of AV adoption in Step 6.
Results were discussed and policy recommendations were proposed to encourage peo-
ple to use AVs and to increase the market penetration of AVs in Step 7. Finally in Step 8,
we identified gaps in the literature and provided directions for future research.
Figure 1. Overall structure of this review study.
The rest of this article is outlined as follows. Section 2 introduces our search strate-
gies and the different attributes of the reviewed articles and reports. A synthesis of the
results from previous studies is presented in Section 3. Research gaps and shortcomings
of prior research and directions for future study are examined in Section 4. Finally, con-
clusions are drawn in Section 5.
2. Materials and Methods
2.1. Study Approach
This state-of-the-art literature review is conducted to identify, evaluate, and critical-
ly analyze relevant scholarship in order to understand people’s perceptions and opinions
about AVs and to identify the factors that influence AV adoption. The overall study ap-
Figure 1. Overall structure of this review study.
The rest of this article is outlined as follows. Section 2introduces our search strategies
and the different attributes of the reviewed articles and reports. A synthesis of the results
from previous studies is presented in Section 3. Research gaps and shortcomings of prior
research and directions for future study are examined in Section 4. Finally, conclusions are
drawn in Section 5.
2. Materials and Methods
2.1. Study Approach
This state-of-the-art literature review is conducted to identify, evaluate, and critically
analyze relevant scholarship in order to understand people’s perceptions and opinions
about AVs and to identify the factors that influence AV adoption. The overall study
approach is illustrated in Figure 2. First, a scan of the literature was conducted to select
published articles and reports to be included in the review process. Based on the initial
literature search, some keywords (e.g., autonomous vehicle, connected and autonomous
vehicle, self-driving car, driverless car, public perceptions, opinions, willingness, attitude,
opportunities, and challenges) were used as search terms to identify relevant articles.
Sustainability 2023,15, 11541 5 of 29
Widely used databases, such as ScienceDirect, Scopus, SAGE Journals, SpringerLink, Taylor
and Francis, and Web of Science, Google Scholar, and the websites of different organizations,
are the main platforms we used to identify articles and reports suitable for inclusion in the
review process. Items were selected based on these additional criteria:
Sustainability 2023, 15, x FOR PEER REVIEW 5 of 30
proach is illustrated in Figure 2. First, a scan of the literature was conducted to select
published articles and reports to be included in the review process. Based on the initial
literature search, some keywords (e.g., autonomous vehicle, connected and autonomous
vehicle, self-driving car, driverless car, public perceptions, opinions, willingness, atti-
tude, opportunities, and challenges) were used as search terms to identify relevant arti-
cles. Widely used databases, such as ScienceDirect, Scopus, SAGE Journals, Spring-
erLink, Taylor and Francis, and Web of Science, Google Scholar, and the websites of dif-
ferent organizations, are the main platforms we used to identify articles and reports
suitable for inclusion in the review process. Items were selected based on these addi-
tional criteria:
(1) Whether the article/report is written in English;
(2) Whether the study was conducted in or after 2015; and
(3) Whether the study evaluated perceptions and opinions on AVs.
During the literature search, the following search strings were used to retrieve rele-
vant articles from each of the databases: (“autonomous vehicle” OR “connected and au-
tonomous vehicle” OR “self-driving car” OR “driverless car”) AND (“public percep-
tions” OR “opinions” OR “willingness” OR “attitude” OR “opportunities”, “challenges”)
AND English. The articles and reports published in or after 2015 were primarily selected
for this review study due to rapid changes in AV technologies and the simultaneous
changes in people’s behaviors.
Figure 2. Selection procedure of scholarly work for the study approach.
A few studies conducted before 2015 are included in this review for a more com-
prehensive scan of scenarios and technological developments related to AVs and Con-
nected and Autonomous Vehicles (CAVs). The search identified more than 140 articles
and reports. However, after closer examination of the abstract and full text, and after
manually removing the duplicate records, 81 were deemed pertinent to the objectives of
this study and are included in this state-of-the-art review study. Some articles and re-
ports were excluded from the list due to unavailability of the full text and due to not be-
ing written in English. Of these items, 43% were published in just two periodicals, name-
ly Transportation Research Part C: Emerging Technologies (23%) and Transportation Re-
search Part F: Traffic Psychology and Behaviour (20%). About 93.80% of the selected
items were published between 2015 and 2023, with the rest being published before 2015.
During the process of filtering published works, the researchers were careful to select
them from different study contexts in order to obtain a comprehensive review. Finally,
these research items were critically analyzed in order to assess their contribution to the
understanding of public perceptions and opinions on AVs and of the factors that influ-
ence AV adoption.
Figure 2. Selection procedure of scholarly work for the study approach.
(1)
Whether the article/report is written in English;
(2)
Whether the study was conducted in or after 2015; and
(3)
Whether the study evaluated perceptions and opinions on AVs.
During the literature search, the following search strings were used to retrieve rel-
evant articles from each of the databases: (“autonomous vehicle” OR “connected and
autonomous vehicle” OR “self-driving car” OR “driverless car”) AND (“public percep-
tions” OR “opinions” OR “willingness” OR “attitude” OR “opportunities”, “challenges”)
AND English. The articles and reports published in or after 2015 were primarily selected for
this review study due to rapid changes in AV technologies and the simultaneous changes
in people’s behaviors.
A few studies conducted before 2015 are included in this review for a more compre-
hensive scan of scenarios and technological developments related to AVs and Connected
and Autonomous Vehicles (CAVs). The search identified more than 140 articles and reports.
However, after closer examination of the abstract and full text, and after manually removing
the duplicate records, 81 were deemed pertinent to the objectives of this study and are
included in this state-of-the-art review study. Some articles and reports were excluded from
the list due to unavailability of the full text and due to not being written in English. Of these
items, 43% were published in just two periodicals, namely Transportation Research Part C:
Emerging Technologies (23%) and Transportation Research Part F: Traffic Psychology and
Behaviour (20%). About 93.80% of the selected items were published between 2015 and
2023, with the rest being published before 2015. During the process of filtering published
works, the researchers were careful to select them from different study contexts in order to
obtain a comprehensive review. Finally, these research items were critically analyzed in
order to assess their contribution to the understanding of public perceptions and opinions
on AVs and of the factors that influence AV adoption.
2.2. Attributes of Reviewed Articles and Reports
Different attributes (e.g., authors, study contexts, data sources, sample sizes, and
methods) of the articles and reports reviewed here are reported in Table 1. The table
indicates that 32.31%, 27.69%, and 26.15% of articles/reports have been conducted on
North American, European, and Asian countries, respectively; 13.85% of studies are about
the Australian context. Most studies (80%) conducted web-based or face-to-face household
questionnaire surveys to collect primary data on people’s perceptions and opinions on AVs.
However, a handful of studies (14%) performed experiments and collected data from the
Sustainability 2023,15, 11541 6 of 29
participants of driving simulators. There is high variability in sample sizes across studies.
The smallest sample (i.e., 19) is used in Hilgarter and Granig [
24
], while Shin, Tada [
33
]
collected data on 246,642 individuals, which adequately represented the population of
the study area. Table 1also indicates that studies have used a variety of statistical and
econometric models to conceptualize people’s perceptions of AVs and associated factors.
Table 1. Characteristics of reviewed articles and reports.
Author Study Area Data Source Sample Size Methodologies
[1] Germany and California Online survey 536 FA, SEM, LRM
[2] Texas, US Online survey 1088 OPM
[8] US Online survey 1260 CLM, PRPLM, SRPLM
[11] Pennsylvania, US General public survey 798 DS
[12] Berkeley, California Opinion of museum visitors 107 MNL, LLM
[17] US, UK, and Australia Online survey 1533 DS, ANOVA
[24] Austria Face-to-face interviews 19
DS, qualitative analysis
[25] US Online survey 2588 MNL
[26] UK Experimental study 30 ANOVA, PC
[33] Japan Online survey 246,642 MLR, OLR
[34] Athens, Greece Online survey 483 SEM, FA
[35] Xi’an, China Participants in a field test 300 SEM, FA, MLR
[36] Boston, MA Participants in driving
simulator, online survey 430 SEM, FA, MLR
[37] Austin, US Online survey 347 OPM, SUM
[38] 109 countries Online survey 4886 DS
[39] China, India, and Japan Online survey 1722 DS, ANOVA
[40] Experts around the world Expert opinions from AV
Symposium, 2014 217 DS
[41] London, UK Survey of transport
professionals 3500 DS
[42] 112 countries Online survey 8862 DS
[43] La Rochelle, France Online and phone survey 425 DS
[44] Vantaa, Finland Participants with experience
of driverless shuttle 197 DS, ANOVA
[45] Six cities in Korea Stated preference survey 633 MDCP, MNP
[46]Adelaide, Brisbane,
Melbourne, Perth, Sydney Stated preference survey 435 MLM
[47] Israel and North America Stated preference survey 721 LKM, FA
[48] 33 countries Online survey 489 DS
[49] Washington, US Travel survey 2726 OPM, SEM
[50] Atlanta, US Travel survey 10,278 LRM, MIP
[51] Memphis, US Questionnaire survey 327 DS
[52] Germany Online survey 501 SEM
[53] China Questionnaire survey 647 SEM
[54] Bangladesh Online survey 621 MLR
[55]Toronto and Hamilton
Area, Canada Online survey 3201 PM
Sustainability 2023,15, 11541 7 of 29
Table 1. Cont.
Author Study Area Data Source Sample Size Methodologies
[56] US Online survey 2167 BLM, WMNL
[57] Brisbane, Australia Household survey 447 MNL
[58] Adelaide, Australia Online survey 101 FA
[59] UK Online survey 916 MNL
[60] Australia Online survey 505 MLR
[61] China Online survey 1164 DS, ANOVA
[62] Germany Experimental study 59 ANOVA, HLM
[63] US Stated preference survey 1390 SEM
[64] Eight European countries Online survey 9118 FA, SEM
[65] Germany Experimental study 101 DS, FA, ANOVA
[66] Korea Experimental study 48 DS, FA, ANOVA, MLR
[67] Singapore Face-to-face interviews 353 FA, SEM
[68] Beijing, China Face-to-face interviews 355 FA, SEM
[69] US Online survey 721 FA, MNL
[70] Taiwan Face-to-face interviews 700 FA, SEM, ANOVA
[71] Seoul, Republic of Korea Online survey 526 FA, SEM
[72] Brussels, Belgium Online survey 529 DS, HLM
[73] Austin, TX, US Online survey 556 DS
DS = Descriptive Statistics, ANOVA = Analysis of Variance, PC = Pearson Correlation, SEM = Structural
Equation Model, FA = Factor Analysis, MLR = Multiple Linear Regression, BLM = Binary Logit Model,
MNL = Multinomial
Logit, WMNL = Weighted Multinomial Logit Model, PM = Probit Model, OPM = Or-
dered Probit Model,
OLR = Ordered
Logistic Regression, SUM = Seemingly Unrelated Model, MDCP = Multiple
Discrete–Continuous Probit, MNP = Multinomial Probit Model, MLM = Mixed Logit Model, LKM = Logit Kernel
Model,
CLM = Conditional
Logit Model, PRPLM = Parametric Random Parameter Logit Model, SRPLM = Semi-
parametric Random Parameter Logit Model, LLM = Log-Linear Regression, LRM = Logistic Regression Model,
MIP = Mixed-Integer Programming, HLM = Hierarchical Linear Model.
The core themes discussed in the reviewed papers are identified in Figure 3. The
majority of studies (80%) collected socioeconomic information on the respondents and
investigated their effects on the decision-making process with reference to the adoption of
AVs. A considerable number of studies explored people’s awareness and knowledge of AVs
and their features (42%) and opportunities and challenges (40%) towards the increase in AV
market share. A nearly equal number of studies (34% and 32%) investigated the influence
of psychological and transportation factors on AV adoption, respectively. About 24% of
studies discussed people’s inclinations to adopt and use AVs. The influence of urban form
(20%) and technology savviness (14%) on AV adoption was mentioned in a relatively small
number of studies. The condition and effects of institutional settings were described by only
4% of studies. Considering the significant implications of psychological and socioeconomic
attributes, transportation factors, urban form, technological innovations, and institutional
regulations and guidelines in motivating people towards AVs, their detailed discussion in
different study contexts with a diverse background of customers is crucial.
Sustainability 2023,15, 11541 8 of 29
Sustainability 2023, 15, x FOR PEER REVIEW 8 of 30
Figure 3. Core themes discussed in the reviewed articles.
3. Synopsis of the Literature
3.1. Contextualization of the Factors of AV Adoption
A conceptual framework is proposed to cohesively articulate the web of factors that
influence people towards adoption and use of AVs. Figure 4 shows the factors that fit
this framework and the interactions between them. This framework is grounded in the
evidence-based research reported in this literature review. It is centered on the individu-
al person and/or household positioning themselves with respect to the AV mobility op-
tion and associated functionalities by either espousing the adoption and use of AVs or
standing against it. Some factors are internal and pertain to the psychology and cogni-
tion of technological change, or to innovation receptiveness, risk aversion, trust, and
sense of usefulness of AV technologies. Affinity with new technologies also predisposes
individuals towards AVs. For example, people are more interested in adopting and us-
ing certain vehicles if these vehicles are equipped with advanced automation and con-
nectivity technologies (e.g., automated speed control, braking and parking, collision
warning, blind-spot detection, or lane-changing warning). Deeper trust in automation
and in connectivity technologies to safeguard drivers from traffic crashes induces people
to use AVs. Perceived ease of use and usefulness of AVs, prior experience or knowledge
of AVs, attitudes, and perception of AVs significantly influence the willingness of people
to adopt AVs. Thus, evidence supports that all these factors significantly affect the deci-
sion-making process of consumers to adopt and use AVs.
Figure 3. Core themes discussed in the reviewed articles.
3. Synopsis of the Literature
3.1. Contextualization of the Factors of AV Adoption
A conceptual framework is proposed to cohesively articulate the web of factors that
influence people towards adoption and use of AVs. Figure 4shows the factors that fit
this framework and the interactions between them. This framework is grounded in the
evidence-based research reported in this literature review. It is centered on the individual
person and/or household positioning themselves with respect to the AV mobility option
and associated functionalities by either espousing the adoption and use of AVs or standing
against it. Some factors are internal and pertain to the psychology and cognition of techno-
logical change, or to innovation receptiveness, risk aversion, trust, and sense of usefulness
of AV technologies. Affinity with new technologies also predisposes individuals towards
AVs. For example, people are more interested in adopting and using certain vehicles if
these vehicles are equipped with advanced automation and connectivity technologies (e.g.,
automated speed control, braking and parking, collision warning, blind-spot detection, or
lane-changing warning). Deeper trust in automation and in connectivity technologies to
safeguard drivers from traffic crashes induces people to use AVs. Perceived ease of use and
usefulness of AVs, prior experience or knowledge of AVs, attitudes, and perception of AVs
significantly influence the willingness of people to adopt AVs. Thus, evidence supports
that all these factors significantly affect the decision-making process of consumers to adopt
and use AVs.
Other internal factors include various user attributes (i.e., socioeconomic features) that
condition attitudes and willingness to adopt. For example, people with higher income and
higher educational attainment are more willing to adopt and use AVs. User attributes also
affect AV adoption and use indirectly by conditioning psychological factors of potential
users regarding AVs.
Other factors are exogeneous, such as factors of urban form (e.g., urban/rural, density,
land-use diversity), which may also influence AV adoption outcomes by shaping people’s
preferences. For example, all other things being equal, urban residents may be more
inclined to adopt and use AVs than rural populations.
Exogeneous factors also encompass transportation factors (e.g., travel mode, distance,
and time), affordability of new technologies, and the institutional context of public policies
and politics. For example, frequent users of public transportation systems may be more
likely to also use SAVs owing to the shared properties between these modes, while people
who drive to destinations are more interested in owning their own personal AVs. The
affordable access to cutting-edge AV safety technologies could also motivate people to use
AVs. The ambient technologies, their affordability, and deep-seated and learned personal
Sustainability 2023,15, 11541 9 of 29
attitudes towards them form a powerful, dynamic, socio-technical context out of which
opportunities and challenges of AVs are articulated by potential users. These, in turn,
frame their willingness to use and adopt AV technologies. Similarly, effective institutional
supports (e.g., precise and updated traffic regulations, financial incentives) could positively
affect people’s AV adoption tendencies. The various considerations boxed together in
Figure 3are discussed in turn in the subsequent sections.
Sustainability 2023, 15, x FOR PEER REVIEW 9 of 30
Figure 4. Contextualization of the factors that influence AV adoption and use.
Other internal factors include various user attributes (i.e., socioeconomic features)
that condition attitudes and willingness to adopt. For example, people with higher in-
come and higher educational attainment are more willing to adopt and use AVs. User at-
tributes also affect AV adoption and use indirectly by conditioning psychological factors
of potential users regarding AVs.
Other factors are exogeneous, such as factors of urban form (e.g., urban/rural, den-
sity, land-use diversity), which may also influence AV adoption outcomes by shaping
people’s preferences. For example, all other things being equal, urban residents may be
more inclined to adopt and use AVs than rural populations.
Exogeneous factors also encompass transportation factors (e.g., travel mode, dis-
tance, and time), affordability of new technologies, and the institutional context of public
policies and politics. For example, frequent users of public transportation systems may
be more likely to also use SAVs owing to the shared properties between these modes,
while people who drive to destinations are more interested in owning their own person-
al AVs. The affordable access to cutting-edge AV safety technologies could also motivate
people to use AVs. The ambient technologies, their affordability, and deep-seated and
learned personal attitudes towards them form a powerful, dynamic, socio-technical con-
Figure 4. Contextualization of the factors that influence AV adoption and use.
3.2. People’s Willingness to Use AVs and Associated Factors
Many studies have investigated the willingness of people to expand their transporta-
tion budget to take advantage of AV functionalities and have found a certain reluctance
to do so [
48
,
49
,
56
]. They show that people are more interested in riding in an AV than in
owning or leasing one [
48
]. However, despite the higher price of personal AVs in the near
term, close to half the respondents (48.72%) in Washington State showed an interest in them
for commuting purposes due to their greater convenience [
49
]. Surveying potential users
in the US, Bansal and Kockelman [
56
] also found that 45.8 to 50.7% of respondents showed
a certain interest in AV technologies. More specifically, 24 to 57% of respondents would like
Sustainability 2023,15, 11541 10 of 29
to add Level 3 or Level 4 automation to their vehicles [
37
], and 41 to 59% of respondents
are interested in owning or sharing AVs in Austin, TX [73].
As indicated in more detail in Table 2, studies have found several factors that rather
consistently stand out as meaningful controls of people’s willingness to use AVs. These
include several socioeconomic traits (e.g., higher household income, children in the house-
hold), personal attitudes like being tech savvy, mobility conditions (car ownership, driving
alone, disability status (given the travel assistance benefits provided by AVs), well con-
nected street networks, being permitted to drive AVs on local streets as well as on freeways),
and technological advancements embedded in AVs (higher traffic safety and reduced bur-
den of driving) [
33
,
37
,
45
]. In contrast, the most prominent factors that dampen people’s
willingness to use AVs include certain socioeconomic traits (holding a driver’s license),
personal attitudes (security concerns, ride sharing attitudes), mobility aspects (driver’s
license, AV license to drive on local roads only), costs (vehicle purchase and operational
cost), and the technological advancement of AVs (cybersecurity) [
25
,
33
,
37
]. Studies suggest
that a drop in purchase and operating costs would increase people’s willingness to use
AVs. For example, reducing travel costs to USD 1/mile from USD 3/mile can increase
people’s interest in using AVs from 3% to 41% in the US [
37
]. Thus, it is expected that
overall ownership and maintenance costs will strongly determine future adoption and use
of AVs.
Table 2. Factors influencing people’s willingness to use AVs (selected studies).
Study Positive Factors Negative Factors
[2]
Familiarity with the Google car, being supportive of
government intervention, high income, higher VMT,
experienced fatal crashes, digital connectivity.
Holding a driver’s license, being elderly, living
in a dense area, living far away from transit
stations, familiarity with ride-sourcing services.
[25]Long-distance business travel, high income, college
educated, employment density.
Higher travel time, elderly, presence of a worker
in household, holding a driver’s license,
population density.
[33]
Male, travel assistance for elderly, high income,
children in household, car ownership, availability of
AV features.
Higher purchase and maintenance costs,
information leakage to third parties, long travel
time, driving on local roads, holding a driver’s
license.
[37]
Social acceptance, reliability, high income, tech savvy,
presence of children, driving alone, urban living,
higher VMT, long commute.
Holding a driver’s license, living in job-dense
areas, being elderly, familiarity with carsharing
and ridesharing.
[38]
Higher VMT, experience with automatic cruise control
feature, male, higher income. -
[45] Cutting edge AV features. High purchase price, concerns about safety.
[55]
High income, male, possession of a smartphone,
employment density, familiarity with and user of
shared mobility.
Unaware of the Google car.
[56]Long travel distance, experienced with
automated features. -
[57]High income, environmentally aware, open to public
transport and ride-sharing options. -
Considering the attitudes and perceptions of populations along with other factors,
researchers have reported that many people would be interested in adopting the novel
technologies embedded in AVs. These studies have applied various existing cognition and
decision theories, such as TRA, TPB, and TAM [
74
–
76
], to conceptualize and understand
the factors that influence people’s behavioral intention (BI) (i.e., willingness) to adopt AVs.
According to these theories, human BI toward actual AV use is directly influenced by
behavioral control factors (e.g., socioeconomic and travel factors), objective factors (i.e.,
Sustainability 2023,15, 11541 11 of 29
urban form), and psychological factors (i.e., perceived usefulness and perceived ease of
use). Additionally, these models indicate that the actual use of AVs also depends on the
availability of novel technologies (e.g., EV, solar panel) and people’s affinities towards
them. Socioeconomic factors also indirectly affect AV use by influencing objective factors,
psychological factors, and the affinity of people towards technology.
3.3. Psychological Factors
A number of studies have investigated the behavioral intentions to adopt and use AVs
by estimating the impacts of perceived usefulness (PU), perceived trust (PT), perceived ease
of use (PEU), social influence (SI), and traffic safety (TS). These studies have demonstrated
the critical role of a rich array of psychological factors, as indicated below. Compared to
other factors (e.g., socioeconomic and demographic, built environment), psychological
factors alone explain 43.7% [
34
], 67.8% [
60
], 69% [
71
], 71% [
36
], and 76% [
52
] of the variation
in people’s BI towards AVs. Xu, Zhang [
35
] estimated that sociodemographic factors
(e.g., age, gender, income, and driving experience) have a very limited influence on BI
to AV adoption compared to psychological factors, which is also supported by other
studies [
52
,
53
]. Table 3shows the impacts of different psychological factors on BI towards
AVs, as expressed by their standardized coefficients.
Table 3. Impacts (standardized coefficients) of psychological factors on BI to adopt AVs.
Studies PU PT PEU SI TS PR PBC TA PS
[1] 0.49 0.29
[34] 0.52 0.15 0.13 0.14
[35] 0.43 0.12 0.19 0.14
[36] 0.80 0.13 0.10
[52] 0.23 −0.05 0.17 −0.17 −0.28
[53] 0.13 0.37 0.14 0.10
[59]−0.24
[60] 0.64 0.30 −0.05
[64] 0.14 0.05 0.40
[67]−0.11
[68] 0.42 0.09 −0.11
[70] 0.35 0.04
[71] 0.45 0.47
BI = Behavioral Intention, PU = Perceived Usefulness, PT = Perceived Trust, PEU = Perceived Ease of Use,
SI = Social
Influence, TS = Traffic Safety, PR = Perceived Risk, PBC = Perceived Behavioral Control, TA = Technol-
ogy Anxiety, PS = Price Sensitivity.
Table 3indicates that PU has the strongest impact on BI compared to other factors. A
sense of usefulness garnered by adding autonomous features to vehicles such as adaptive
cruise control (ACC), self-parking assistance, and voice interactions positively influences
people’s BI to use AVs [
26
,
45
,
64
]. Usefulness also increases when people can engage in
other activities (e.g., talking on the phone, reading, working, responding to email, engaging
in social media, or checking the day’s news) while riding in an AV [
54
]. Additionally, the
PEU of AVs has a significantly positive effect on their PU [
70
], and familiarity with smart
phone and smart vehicle technologies, prior knowledge, and experience of AVs could all
increase the impacts of PU and PEU on BI [
26
,
64
]. Xu, Zhang [
35
], in a study examining
drivers before and after AV experience, mentioned that prior AV experience increases PU
by 0.08 units, PEU by 0.12 units, and BI by 0.02 units.
Researchers have identified PT of technology as one of the most important psychologi-
cal factors that induce people to adopt AVs (Table 3). Vehicles equipped with advanced
driver-assistance systems (ADAS) raise the trust of users by reducing the probability of
crashes and by increasing the controllability of risky driving compared to vehicles without
ADAS technology [
1
,
65
,
66
]. Moreover, an external human–machine interface that displays
information could increase BI towards AVs by increasing safety, trust, intelligence, and
transparency [
62
]. Trust rises when AVs become predictable and understandable, when
Sustainability 2023,15, 11541 12 of 29
they complete tasks accurately and correctly, and when they allow users to take control of
the vehicle when they so desire [
47
,
71
]. Confidence in intelligent robots, perceived social
benefits, and structural assurance of technologies from implemented policies and regula-
tions are essential to building initial trust in AVs [
77
]. Researchers [
53
] have mentioned
that personality traits have a significant influence on the trust that individuals place in AVs.
For example, open-mindedness and sensation seeking have a positive effect on trust, while
neuroticism (i.e., frequently changing mode) and agreeableness have a negative effect on
trust. Thus, it is imperative to solidify the trust of users by promoting safety measures
of AVs rather than by only focusing on usefulness and ease of use in order to increase
acceptance of AVs.
PR has been reported to affect the BI towards AVs (Table 3). The fear of crashes, cyber-
attack, operating speed, inclement weather, and the apprehension about sharing space in
an AV with strangers could be the main causes of perceived risk. People perceive a higher
risk when AVs are operated at a slow speed on a clear day, whereas people perceive a
lower risk when AVs are operated at a slow speed on a snowy night [
66
]. Also, self-identity
concern (i.e., AV is a threat to their personal identity as a driver) adversely influences
users’ willingness to use AV technologies [
67
]. People are emotionally concerned about the
perceived invasion of their personal space when an AV is shared with strangers [
69
]. For
these various reasons, people are inclined to show a negative disposition towards AVs.
Many studies have reported that social norm and conformity (i.e., influence from
relatives, friends, and neighbors) imposes checks on BI to use AV (Table 3). Bansal and
Kockelman [
2
] found that 47% of Texans are willing to adopt AVs when their friends also do
so, and similar results are reported by Bansal, Kockelman [
37
]. Hence, people’s willingness
to use AVs is partly influenced by social norms and the perception of AVs as status symbols.
Social influence positively affects PU, PEU, and PT, and consequently determines whether
people would use AVs or not [53].
The higher price of the vehicle and the higher usage costs could negatively affect BI to
use AVs. Kapser and Abdelrahman [
52
] reported that price sensitivity has the strongest
influence on BI to use AVs compared to other factors like performance expectancy, hedonic
motivation, perceived risk, social influence, and facilitating conditions.
Some studies have delved deeper in order to single out the cognitive concepts that
shape the views on AVs and AV functionality. It is mentioned that the confidence and
self-efficacy of users (i.e., belief in one’s own capability to handle an AV), the perceived
advantages of AVs over conventional vehicles, the observability of the benefits of using
AVs, the compatibility of smart vehicle technologies with one’s lifestyle, past experiences,
and travel needs), the possibility to test AVs before their actual use, and pro-AV attitudes
directly drive people’s intentions towards adopt and use AVs [
68
,
70
,
71
]. Also, extant
studies have observed that enjoyment, comfort and convenience, and hedonic motivation
(i.e., fun, enjoyable, and entertaining) positively influence people’s BI to use AVs [
52
,
70
,
72
].
People’s willingness to adopt and use AVs also increases because of the perceived value
(i.e., superior benefits) and the performance of AVs [
36
,
71
]. Besides the direct effects, the
perceived value also indirectly increases people’s BI by raising trust and reducing risks;
this pathway operates by addressing individuals’ expectations, offering more incentives,
and increasing safety and reliability. In contrast, when the car experience is dominated by
emotions of fascination with luxury, image, and prestige, then the loss of vehicle control
and the greater complexity of the vehicle reduce people’s intentions to use AVs [12,18].
To sum up, previous studies have underscored that PU, PT PEU, SI, and TS motivate
people to use AVs, while PR, TA, and high price turn their intentions against AV use. Thus,
psychological factors make a significant contribution to defining people’s BI to adopt AVs.
3.4. People’s Attitudes and Perceptions of AVs
Some studies have specifically investigated people’s attitudes (i.e., positive, negative)
towards AVs. On the basis of user opinion surveys, it is often found that most people
have a positive intention to adopt and use AVs owing to the absence of barriers to their
Sustainability 2023,15, 11541 13 of 29
ubiquitous use in the population, to various amenities for multitasking while riding, to
cutting-edge technologies, and to the potential for better traffic safety [
12
,
69
,
72
]. Surveys
in multiple countries point out that 52.2 to 61.9% of respondents in Australia, the US, and
the UK [
17
], and 43 to 87.4% of respondents in China, India, and Japan [
39
] have a positive
impression of vehicle automation. Conversely, only 11.3 to 16.4% of respondents have some
negative impression in Australia, the US, and the UK, due in large part to legal liabilities,
privacy concerns, and safety issues [
17
]. Investigating positive and negative attitudes
towards automated driving in 112 countries, Bazilinskyy, Kyriakidis [
42
] found that 39% of
respondents showed a positive attitude and only 23% showed a negative attitude to AVs.
In their study of the US, Wang, Jiang [
69
] found that 36.7% of respondents who
own smart devices and are familiar with AVs have a positive outlook on AVs, and 21.8%
have a negative outlook on shared AVs. However, a large fraction of the population
(44.7%) is not yet ready to use an AV with no driver, and there is overall reluctance
towards sharing a ride in an AV taxi. Over 40% of respondents in Berkeley, California were
positive to either purchasing self-driving technology in their next vehicle or retrofitting
their current vehicle with such technology [
12
]. Overall, 70% of respondents accepted the
technology in California due to its usefulness and ease of use, 30.2% of respondents accepted
it conditionally, and 24.5% were not accepting of the technology at all [
1
]. Surveying
vulnerable road users (e.g., pedestrians, cyclists) in Pittsburgh, PA, Penmetsa, Adanu [
11
]
found that many respondents (nearly 70%) approve of AVs on the street because they did
not find any difference between AVs and human-operated vehicles and did not experience
any negative interaction with AVs (i.e., unexpected maneuvering of AVs). However, some
researchers [
2
,
69
] have argued that many Americans are not yet confident and ready to use
AVs for work and non-work trips due to associated legal and safety uncertainties, but they
would still be major consumers of AVs compared to people from other parts of the world.
Researchers in Athens, Greece [
34
] found that 58% and 12% of respondents have
positive and negative perceptions about AVs, respectively. Piao, McDonald [
43
] observed
that 66.67% of respondents in the city of La Rochelle, France, would like to experience
automated buses, even if there are human-operated buses on the street. These studies
demonstrate that people’s perceptions of AVs are more positive than negative, and people
are interested in using AVs in the near future as they seem safe, comfortable, fun, and easy
to navigate, despite some uncertainties (e.g., emergency reactions, technical failure, and
vulnerability to cyber-attack) [72].
People’s attitudes and perceptions on AVs have a commanding hold on the anticipated
opportunities and challenges that individuals associate with AVs. Admittedly, this is a
direct influence, but another set of more complex (indirect) influences are mediated by
other internal factors. The opportunities that people envision can strengthen their willing-
ness to use and adopt AVs as a pathway to capitalize on their technological possibilities
in a real-world context. On the other hand, anticipated challenges can create negative
perceptions and deter people from adopting AVs. However, with the benefit of time, these
challenges could be turned into opportunities once ways to provide appropriate solutions
are identified. Thus, understanding the challenges properly is necessary for the rapid
development and adoption of AVs.
3.5. Opportunities and Challenges to Adopting Autonomous Vehicles
Many studies have striven to identify the opportunities and challenges of AV deploy-
ment that affect people’s intentions to adopt and use these technologies. Drawing on the
findings from previous literature, different social, economic, transportation, environmental,
technical, legal, and institutional opportunities and challenges are outlined in Table 4in
regard to the successful development and implementation of AVs. Opportunities in and
challenges to adopting AVs are expressed as the percentage of mentions by respondents.
Sustainability 2023,15, 11541 14 of 29
Table 4. Respondent opinions on opportunities in and challenges to adopting AVs.
Author Opportunities (%) Challenges (%)
[1]
Reliability (California: 30.1%, Germany: 25.0%),
problems when entering/exiting the highway (Cal:
23.9%, Ger 25.4%), issues with cut-in vehicles (Cal:
15.3%, Ger: 18.7%)
[2]
Talking to others (59.5%), looking out the window
(59.4%), fuel economy (53.9%), crash reduction (53.1%),
emergency notification (71.5%), vehicle health
reporting (68.5%), use of AVs for all trips (33.9%) and
social or recreational trips (24.7%)
Street congestion (36.1%)
[11]
Improved traffic safety (62%), safe to share with other
modes of transportation (43%), reduced traffic
fatalities and injuries (67%)
Set regulation for AV testing (70%)
[12]
Safety (75%), convenience (61%), amenities (e.g.,
ability to text messages or multitask while
riding) (53%)
Liability (70%), cost (60%), lack of control (53%)
[17]
Fuel economy (72%), travel time-savings (43%), few
crashes (70.4%), reduced crash severity (71.4%),
improved emergency response (66.9%), low emission
(66.3%), low insurance cost (55.5%), less traffic
congestion (51.8%)
System failure (80.7%), legal liability (74.1%), system
security (68.7%), vehicle security (67.8%), data privacy
(63.7%), interacting with conventional vehicles (69.7%),
interacting with pedestrians/bicyclists (69.8%), learning
to use AV (53.5%), system performance in poor weather
conditions (62.8%), unexpected situations (75.7%), no
driver control (54.3%)
[24] Feelings of safety (84.2%) Lack of confidence in technology (10.5%)
[25]Comfortable with data sharing for policy
purpose (48%)
Privacy concerns (89%), unwilling to pay to anonymize
location (39.8%), oppose data sharing for advertising
purposes (50%)
[33]
Reduced traffic crashes and improved comfort and
convenience (37.3%), no need for driver’s license
(12%), reduced mobility and crashes related to
problems of elderly persons (50%)
Technological dependability (43.48%), vehicle safety
(31.43%) of full AV, cost of new and not-yet-available
technology (25.26%)
[34]
Solution to many problems (88%), easy to operate
(64%), clear and understandable interaction (69%),
easy to become skillful (66%), useful to meet driving
needs (46%), safe travel (44%), interesting travel
(38.3%), few crashes (55.3%)
Safety concerns (55%), waste of time (65.6%), make life
more complicated (58.8%), do not increase social
status (33%)
[37]
Reduction in crashes (63%), talk or text to others (75%),
surf the internet (36%), email while driving (45.2%)
Interactions with conventional vehicles (48%),
affordability (38%), equipment or system failure (50%)
[39]
China: Few crashes (85.7%), reduced crash severity
(85.1%), improved emergency response to crash
(88.8%), shorter travel time (78.3%), low insurance
cost (78.5%).
India: less traffic congestion (72.3%), better fuel
economy (85.9%)
China: system failure (68.0%), legal liability (55.1%),
interacting with pedestrians and bicyclists (42.6%),
system performance in poor weather (59.6%), AVs
confused by unexpected situations (56.1%)
India: system security (54.6%), vehicle security (57.3%),
data privacy (50.9%), learning to use AVs (43.6%)
[43]
Increased mobility (58%), reduced fuel consumption
and emission (56%), low bus fares (64%), low
insurance rates (53%), low parking costs (49%), safer
driving (36%), reduced taxi fares (36%), allows users to
do other things (20%), improved safety (80% for
automated bus, 89% for automated car)
Equipment/system failures (66%), legal liability (56%),
vehicle security (54%)
[49] Reduced congestion (22.96%)
Sustainability 2023,15, 11541 15 of 29
Table 4. Cont.
Author Opportunities (%) Challenges (%)
[56]
Enjoyable (75.7%); advanced technology (54.4%);
comfortable (19.5%); reliable (49%); omnipresent in
future (41.4%); comfortable to transmit information to
other vehicles (50.4%), to vehicle manufacturers
(42.9%), to insurance companies (36.4%), and to toll
operators (33.3%); trust technology companies (62.3%)
and luxury vehicle manufacturers (49.5%); willing to
use for everyday trips (40%)
Fear of technology (58.4%), not realistic (44%), unwilling
to use for short distance (42.5%) and long-distance
(40%) trips
[60]
Reduction of human error in crashes (35.64%),
multi-tasking (30%), reduction of risk-taking
behaviors (29.3%)
High cost (59.21%), lack of trust (32.1%), no control of
vehicle (37.22%), technology malfunction (34.26%),
safety for self and others (20%), safety of vehicle
(21.39%), loss of driving skill (14.1%)
[61]
Trust (51.32%), lower insurance rates (45.28%), willing
to pay more (69.24%) Increased risk (43.86%)
[63]
Improved safety (43.3%), reduced driving stress
(40.6%), better technology (30.8%), collision avoidance
(52.9%), improved fuel efficiency (46.5%), lane-keeping
assistance (26.5%).
Data privacy (58.4%), trust issue (46.6%), reliability
(48.7%), higher travel time (64.8%)
[64]
Easy to use (71.06%), easy to become skillful using
AVs (60.35%), use of travel time for secondary
activities (41.85%), fun to drive (53.21%), enjoyable
(52.54%), use for everyday trips (53.45%), meet daily
mobility needs (53.27%), entertaining (51.04%), reach
destination safely (48.67%)
[70]Novelty technology (75.7%), low pollution (18.4%),
integration with public transportation (3.7%)
[73]
Lack of trust in technology (41%), safety (24%), cost
(22%), concern about using internet and internet enabled
technologies (51%), privacy concerns (71%)
Traffic safety, reliability and confidence in technology, system robustness against
internet instability and virus attacks, and loss of control of one’s car stand out as people’s
prime concerns [
2
,
51
,
58
]. Nazari, Noruzoliaee [
49
] found safety concerns to have the
highest marginal effect on AV adoption (i.e., a one-unit decrease in safety concern may
reduce the willingness to adopt AVs by over 100%). Unattended drop-offs and pick-ups
of children and the anticipated increased number of pedestrian traffic crashes may make
AV adoption more challenging [
58
]. Similar to traffic safety concerns, the lack of personal
data privacy from hackers (i.e., location tracking, surveillance) poses a major threat to the
adoption and use of AVs [
24
,
47
,
63
]. Thus, it is imperative to raise the perception of safety,
security, and privacy of people to boost AV adoption.
A considerable number of studies have mentioned that the current state of immature
development of AV technologies, insufficient institutional infrastructure, and the absence
of integration with the existing traffic environment would cause major legal challenges
for vehicle owners, manufacturers, and insurance companies [
1
,
12
,
48
]. The inadequate
legal resolutions and institutional setup are leading causes of lagging deployment of AV
technologies and of the lower level of acceptance in the public [
24
]. However, people
who are passengers of a vehicle have fewer legal concerns than vehicle drivers due to the
legal liability for the drivers and vehicle owners [
17
]. Many AV users have argued that
manufacturers and operators should be 100% legally liable for any damages to AVs and
SAVs [78].
Moreover, the high price of AVs and their high maintenance cost would inhibit
people—particularly from low- and medium-income groups—from purchasing and using
Sustainability 2023,15, 11541 16 of 29
AVs [
12
,
24
,
51
]. Thus, affordability among certain socioeconomic groups could be another
major challenge to increasing the market share of AVs [2].
Researchers also mentioned that people who value fuel economy [
12
] and greener
transportation [
47
,
51
] are more interested in adopting and using AVs compared to their
counterparts, as AVs are anticipated to reduce energy consumption, pollution, and transport
costs. The same can be said of the possibility afforded by AVs to reduce traffic congestion
and travel time, thus enabling people’s engagement in other activities [
48
,
49
,
63
]. Finally, AVs
are touted for providing mobility to disadvantaged people (e.g., elderly, disabled) [
48
,
51
] and
improved amenities and services [12,63] motivate people to use AVs.
The extant literature shows that the potentiality of AVs to reduce traffic crashes and
congestion, better amenities to engage in other activities, and proper integration with
public transportation could be strong motivations to use AVs. On the other hand, high
costs, security issues, risk of system failure, and violation of personal privacy discourage
people from adopting AVs.
3.6. People’s Knowledge and Experience of AVs
Prior knowledge and awareness of AVs is considered to be a precondition for firm-
ing up one’s disposition towards AVs. The overwhelming majority of people (i.e., 99%)
have never had any experience with a full AV in their life [
36
,
58
]. Furthermore, recent
studies have reported that most people are unfamiliar with AVs at all and are unaware of
autonomous cars that are already plying the streets in a number of cities [
2
,
55
,
79
]. There
is strong evidence that awareness and information on the perceived benefits of AVs may
motivate AV adoption and willingness to pay for AV services [
8
,
24
,
72
]. Thus, there is
an imperative need to better and more comprehensively inform the general population
about AVs and their functionalities in order to level the playing field and increase their
market share.
The status of people’s basic knowledge of AVs across various studies is summarized
in Table 5. Although it shows that many people (from 49 to 98.8%) have heard of AVs, in
reality, most of them have very limited conscious awareness of this technology and have
seldom experienced AV rides. Knowledge of AVs and of their level of autonomy is lacking
due to the limited availability of AVs for private use. Instead, people mainly receive generic
information on AVs from mass media and social media, which indicates that AVs are still
not a tangible reality that people can relate to [68].
Table 5. Basic knowledge on AVs.
Author Heard of AVs (%)
[2] 59%
[17] 66% overall (70.9% in US, 66% in UK and 61% in Australia)
[34] 71.4%
[35] 94.3%
[36] 63%
[37] 80% (Google car), 47% (CAV)
[38] 52.2%
[39] 87% (China), 73.8% (India), 57.4% (Japan)
[43] 87%
[48] Over 95%
[52] 49%
[53] 98.8%
[54] 90%
[60] 78.4%
[61] 94.67%
Conducting a survey in eight European countries, Nordhoff, Louw [
64
] investigated
the experience drivers had with different ADAS features. Collecting data from Nordhoff,
Louw [
64
], we prepared Figure 5and tried to understand the experience drivers had with
Sustainability 2023,15, 11541 17 of 29
different ADAS features. While features like parking assistance and adaptive cruise control
are now increasingly common in vehicles, most drivers do not have any ADAS, but 47–64%
of survey respondents expected to have them in their vehicles and use them in the future.
Nevertheless, they have enough awareness of ADAS features to confirm their affinity
towards advanced technology, which may translate into adoption in the near future.
Sustainability 2023, 15, x FOR PEER REVIEW 17 of 30
population about AVs and their functionalities in order to level the playing field and in-
crease their market share.
The status of people’s basic knowledge of AVs across various studies is summarized
in Table 5. Although it shows that many people (from 49 to 98.8%) have heard of AVs, in
reality, most of them have very limited conscious awareness of this technology and have
seldom experienced AV rides. Knowledge of AVs and of their level of autonomy is lack-
ing due to the limited availability of AVs for private use. Instead, people mainly receive
generic information on AVs from mass media and social media, which indicates that AVs
are still not a tangible reality that people can relate to [68].
Table 5. Basic knowledge on AVs.
Author Heard of AVs (%)
[2] 59%
[17] 66% overall (70.9% in US, 66% in UK and 61% in Australia)
[34] 71.4%
[35] 94.3%
[36] 63%
[37] 80% (Google car), 47% (CAV)
[38] 52.2%
[39] 87% (China), 73.8% (India), 57.4% (Japan)
[43] 87%
[48] Over 95%
[52] 49%
[53] 98.8%
[54] 90%
[60] 78.4%
[61] 94.67%
Conducting a survey in eight European countries, Nordhoff, Louw [64] investigated
the experience drivers had with different ADAS features. Collecting data from Nordhoff,
Louw [64], we prepared Figure 5 and tried to understand the experience drivers had
with different ADAS features. While features like parking assistance and adaptive cruise
control are now increasingly common in vehicles, most drivers do not have any ADAS,
but 47–64% of survey respondents expected to have them in their vehicles and use them
in the future. Nevertheless, they have enough awareness of ADAS features to confirm
their affinity towards advanced technology, which may translate into adoption in the
near future.
Figure 5. Experience with ADAS, after Nordhoff, Louw [64].
3.7. Socioeconomic Features
The socioeconomic community of each individual tends to predispose them vis-à-vis
adopting AVs above and beyond the intrinsic effect of individual factors rooted in aware-
ness, cognition, and psychology. Thus, many studies have explored diverse socioeconomic
features of people and their impact on AV adoption.
3.7.1. Age Differentiation
Most studies have reported that young cohorts of people are more interested in
using AVs, compared to the elderly [
57
,
63
,
64
,
69
,
80
]. Specifically, Panagiotopoulos and
Dimitrakopoulos [
34
] reported that respondents in the 18–40 age cohort (60.1%) are more
likely to adopt and use AVs compared to people over 40 (55.5%). Researchers in [
43
] found
that 56% of respondents aged over 65 would use automated cars compared to 62% aged
18 to 34 and 61% aged between 35 and 64. They also reported that 52% of respondents
aged 18–34 would own an AV compared to 39% aged 34–65, and 43% aged over 65. Thus,
young adults are more interested in adopting and using personal AVs and SAVs than the
elderly [46,47,49].
Modulating the results of the above studies, Shin, Bhat [
45
] reported, however, that
younger people would be less inclined to adopt technologically advanced vehicles (e.g.,
EVs) due to their higher purchase price, low driving range, and accessibility to charging
stations. Also, a few studies have mentioned no significant associations between age and
public acceptance of AVs [
44
,
54
]. Zmud and Sener [
73
] observed a similar AV adoption
trend among younger (less than 30 years) and elderly (65+) persons (i.e., 53% of 30–45 years,
55% of 45–65 years). Researchers argued that elderly people are pragmatists (positive),
while the young are either conservatives (negative and skeptical) or enthusiasts (posi-
tive) [
24
]. Thus, the acceptance and rejection of AVs may, in fact, comprise a balanced
distribution of elderly and young people.
3.7.2. Gender Differentiation
Studies have investigated the disparities in AV adoption and use between males
and females. Gender analysis shows that men are more likely to adopt and use personal
AVs and SAVs compared to women due to their better economic conditions, their affinity
Sustainability 2023,15, 11541 18 of 29
towards technology, and their higher risk threshold towards the perceived safety and
security of AVs [
12
,
49
,
64
]. For example, Piao, McDonald [
43
] found that 64% of males
would use AVs compared to 55% of females; the respective shares were 49% and 39% for
outright ownership. Additionally, many females hold the view that most of the expected
benefits from AVs are unlikely to materialize [
17
]. This can be further qualified, however,
as researchers have argued that the likelihood of AV use by females depends on perceived
safety, in-vehicle security, and emergency management systems [
44
,
57
]. Thus, it is crucial to
improve the safety and security of AVs and their perception in the public in these respects
in order to overcome female apprehensions.
3.7.3. Marital Status
Studies have observed that married couples are more likely to adopt and use AVs
and SAVs compared to single people due to improved safety measures, amenities such
as multi-tasking opportunity, and the ability to share AVs among household members,
which could reduce overall travel costs [
12
,
49
,
57
]. Moreover, married people are usually
economically better off than single and non-married people, which conditions their greater
affinity to personal AVs [
12
]. On the other hand, Gurumurthy and Kockelman [
25
] reported
that single people are more likely to use AVs and SAVs with dynamic ride sharing services,
which have the potential to reduce travel costs. Thus, splitting travel costs by sharing
mobility with ride companions may induce more single individuals to use AVs and SAVs.
3.7.4. Educational Attainment
Many studies have assessed how much educational attainment regulates the AV
adoption rate. The level of education is seen to be positively associated with people’s
intentions to adopt and use AVs and SAVs for personal travel purposes because they may
already know about AVs and are more receptive to new ideas (e.g., shared mobility) and
technologies [
8
,
25
,
49
]. For example, Piao, McDonald [
43
] reported that 71% of respondents
with higher education (bachelor’s degree and above) are interested in using AVs compared
to 52% of respondents with lower education. Moreover, 28% of respondents with higher
education would consider using SAVs compared to 8% among others.
People with higher education perceive that AVs would reduce the number and severity
of traffic crashes, congestion, travel times, and operational cost [
17
]. The perceived benefits
of AVs are relatively greater among the highly educated persons and the users of on-
demand mobility services compared to less educated persons and users of conventional
Internal Combustion Engine (ICE) vehicles [
71
,
81
]. Thus, it can be argued that the level
of education has a significant impact on the AV market share, as demonstrated by a large
number of studies.
3.7.5. Household Income
Among socioeconomic covariates, employment status and household income are
critical factors in determining AV ownership. Many studies have found that household
income is positively associated with AV adoption and use because high-income people can
better afford AVs, and they are more willing to pay a premium for more advanced facilities
in cars [
37
,
63
,
71
]. Zmud and Sener [
73
] found that 56% of people with incomes under
USD 25k are unwilling to use AVs, while 54% of people with incomes in the range of USD
25k–USD 50k are more likely to use AVs. However, people with higher incomes are less
interested in sharing AVs with strangers [
69
]. Overall, low-income people, the unemployed,
homemakers, and retired persons are less likely to adopt and use AVs compared to ICE
vehicles [8,45,49,80,82].
Some studies also mentioned that full-time employment status is positively associated
with AV ownership and use due to a higher ability to pay [
17
,
25
,
49
]. Employed people
are more likely to own and use AVs compared to the unemployed, students, and retired
persons due to the higher purchase prices and operating costs of AVs. Also, single-income
Sustainability 2023,15, 11541 19 of 29
households are less interested in owning and using AVs [
25
]. Thus, along with a higher
household income, employment status is crucial to the adoption and use of AVs.
3.7.6. Household Size and Composition
Some studies also investigated how the size, composition, and type of households
influence AV adoption. These studies have reported that households with children and
disabled persons have a positive disposition towards AVs due to their better safety mea-
sures and driverless services [
8
,
55
,
69
,
80
]. Moreover, people in larger households, and those
from Hispanic and Asian communities, are more interested in AVs and highly appreciate
their advanced technology to improve mobility of the disadvantaged segments of soci-
ety [
12
]. However, some evidence also suggests that some larger households (more than
four members) are less likely to adopt and use AVs and SAVs due to safety and security
reasons [25,63]. Along the same line, it has been reported that some parents with children
are exceedingly cautious with AV and SAV usage due to the perceived heightened safety
risk for children left with reduced parental supervision [
54
,
57
,
73
]. Thus, household size
and composition of the household have a notable influence on the behavioral intention
towards using AVs and SAVs.
In addition, families holding conservative views are less likely to use AVs until they
becomes more mainstream and people gain experience with them [
8
]. Thus, besides the
status of the households, some other factors (e.g., progressive attitudes, technology) also
determine the AV adoption and use tendency of a family.
3.8. Transportation and Travel Factors
Many studies have investigated the impacts of various travel factors on AV adoption
and use. These factors include vehicle ownership, driving habits, shared mobility, modal
choice, travel distance, vehicle costs, operation and maintenance costs, and trip purposes.
This subsection discusses the role of these factors in determining people’s intentions to
adopt AVs.
Some studies have argued that owning a vehicle and how many vehicles are owned
are positively associated with AVs and SAVs due to the availed benefits of cars [
8
,
54
,
69
].
Researchers in [
45
] mentioned that drivers are more interested in alternative fuel vehicles
(e.g., hybrid and electric) than non-drivers due to their familiarity with alternative fuel
vehicles and their proven track record of driving. Consequently, it is assumed that people
who drive regularly have strong preferences for AVs and other alternative fuel vehicles,
compared to people who seldom drive a car [48,73].
Researchers also found that people professing an interest and preference for public
transportation, car sharing, and walking are also favorably disposed towards SAV and
AV technologies due to pro-environmental and multi-modality attitudes [
49
]. Similarly,
researchers have found a slightly higher tendency to use AVs among the people who
walk and carpool (57%) compared to people who drive (52%) [
73
]. As for drivers of cars,
motorcycles, and scooters, the attraction to SAVs is rooted in their interest for ride-sourcing
shared mobility. Some studies also observed that Single Occupancy Vehicle (SOV) drivers
are less likely to adopt AVs than others, considering their habit and preference for driving,
and the potential loss of the excitement and pleasure of driving [
12
,
37
,
57
]. Thus, preferences
for particular travel modes may be critical determinants for AV and SAV adoption.
Gurumurthy and Kockelman [
25
] reported a positive association of long-distance
commute with SAV usage. Some researchers have found that people with higher total daily
VMT are not favorably disposed towards AV technology for daily use [
49
]. Taken together,
this suggests that people would prefer personal AVs for short-distance commuting trips and
SAVs for long-distance business and recreation trips. Some studies have found that travel
time is positively associated with AV use. For example, Rahimi, Azimi [
63
] observed that
daily travel for longer times (above 30 min) have positive effects on AV use due to low travel
costs and the multi-tasking features of AV riding. Similarly, Nazari, Noruzoliaee [
49
] and
Haboucha, Ishaq [
47
] reported that travel time has a positive association with preference
Sustainability 2023,15, 11541 20 of 29
for personal AVs and SAVs. Although a low in and outside vehicle waiting time (around
5 min) has insignificant influence on SAV use [
46
], researchers elsewhere found that the
extra time added to travel time when an SAV is used reduces people’s interest in this travel
mode option [
25
]. Thus, smooth travel with minimum travel and waiting time would
encourage people to use SAVs for their daily travel purposes.
As discussed in Section 3.2, researchers have reported that high purchase, operation,
and maintenance costs dissuade people from traveling by AV and SAV. In contrast, provid-
ing subsidized or free parking at the workplace and close to the workplace may increase
the use of AVs and SAVs by reducing parking costs, parking search time, and walking
distance [
49
]. However, people are more interested in using SAVs than private AVs to
reduce overall travel costs [8,47].
Some studies have also found that people are not keen on using SAVs for discretionary
trips, such as shopping, medical, business, and recreation trips, due to privacy issues [
25
,
46
].
When given the opportunity, people mostly use existing personal ICE vehicles for work
commuting and grocery trips, public transport for traveling to large cities, and bicycles
for free time relaxation trips [
24
]. Still, many respondents consider AVs to be a secondary
mode of transportation, which could delay AV adoption by a majority of people. However,
respondents envision a greater potential for AVs in tourism, healthcare, and last-mile
passenger transportation to and from public transit stations.
In summary, the extant literature shows that different travel factors are likely to
condition people’s intentions to use AVs and SAVs. Yet, people are unlikely to adopt AVs
as their primary household’s means of transportation. An AV would be used for business
and recreation travel purposes first and foremost.
3.9. Impacts of the Built Environment
Many studies have evaluated whether the built environment and its properties may
be associated with AV adoption and use. Researchers have observed that people who live
in urban areas are more likely to adopt and use AVs and SAVs than others because these
new mobility options reduce parking costs and searching time, and because of people’s
openness to accept promising alternatives that can reduce travel externalities (e.g., accidents,
congestion) [
37
,
48
,
49
]. Recent evidence shows that people who live in areas with high
population and employment density (e.g., Central Business District (CBD)) and mixed
land-use are favorably inclined towards AVs [
25
,
55
,
57
]. However, some researchers found
that people who live in urban areas may have a negative attitude towards SAVs due to
their unwillingness to ride with strangers [
69
]. Affluent urban residents can afford to own
a personal AV due to their better socioeconomic condition compared to households who
live in rural areas. On the other hand, people living outside of urban areas may embrace
the availability of SAVs due to the lack of public and nonmotorized transportation in these
environments [
24
]. Researchers also demonstrated that people living or working close
to the infrastructure of alternative fuel vehicles (e.g., charging stations, hydrogen fueling
stations) are more likely to adopt AVs [
82
]. Thus, the built environment provides a context
that may be quite influential in shaping behavioral intentions to adopt AVs and SAVs.
3.10. Impacts of Cutting-Edge Technology
The extant literature suggests that the extensiveness of development and availability
of cutting-edge technologies accelerates the adoption of AVs and SAVs [
45
,
63
,
69
]. The
enhanced services (e.g., convenience, less travel time and cost, high driving range) and
improved safety features enabled by cutting-edge technology motivate people to be pos-
itively disposed towards AVs [
8
,
63
,
83
]. However, a somewhat different scenario is, in
fact, observed in the US, despite being the largest manufacturer of high-technology prod-
ucts [
73
,
84
]. Conducting an online-based survey, Zmud and Sener [
73
] found that 66% of
respondents identified as late adopters of AV technologies, while 13% would only adopt
at the very last moment, considering the uncertainties associated with AVs. In contrast,
only 21% considered themselves to be early adopters (i.e., first to adopt). Thus, it would
Sustainability 2023,15, 11541 21 of 29
appear that most people would wait and observe the trend of AV adoption in the country
before boarding the wagon. However, it is believed that Americans would ultimately be
the first adopters of AVs internationally when these vehicles become available on the road
for public use, considering their greater affinity to new technologies.
3.11. Impacts of Institutional Factors
Recent literature has reported that an effective organizational infrastructure and in-
stitutional framework (e.g., pertinent policies, regulations, financial incentives, research
and development) could positively affect AV adoption and related technologies [
12
,
85
,
86
].
Institutional and policy entrepreneurs consisting of industries, government agencies, busi-
ness organizations, and knowledge institutions can garner public support and implement
policies and strategies to facilitate the growth of AVs [
85
]. Interventions at the national level
(e.g., release of a plan for safety applications in CAVs) and the state level (e.g., research
and testing), in addition to initiatives from auto manufacturers, are crucial for increasing
the market share of AVs [
87
]. Conducting an online survey in the US, Wang, Jiang [
69
]
reported that people who support stringent traffic regulations (e.g., lower speed limits,
higher speeding fines) have a positive disposition towards adopting and using AVs. Thus,
state and city authorities should implement efficient institutional regulations to manage
transportation systems and provide adequate infrastructure to support the increase in the
market share of AVs.
4. Discussion
4.1. Summary
Considering the higher social and environmental costs of conventional vehicles to
individuals, decision makers are thinking of the possible introduction of AVs, and this
alternative mode of transportation will shortly be a reality. Considering the critical role of
the users, this study investigated the perceptions and opinions of people and identified the
factors that influence them to adopt and use AVs through a review of the extant literature. A
strategic literature search was conducted to select articles and reports for this review. Most
of the articles were published within the last five years and used a household questionnaire
survey to collect data. Mostly, they used statistical and econometric methods to evaluate
the factors that affect people’s intentions to adopt AVs.
The review results show that various user socioeconomic features, knowledge and
familiarity with AV technologies, and psychological factors (e.g., usefulness, ease of use,
trust, risk) would affect people’s AV adoption tendency. User attributes also affect AV
adoption indirectly by influencing their psychological factors. This study identified critical
opportunities (e.g., safety and security, low congestion, energy use, and emissions) and
challenges (e.g., system failure, privacy breaches, and legal issues) that stand out in AV
adoption decisions. External factors, such as urban form (e.g., urban/rural, density, land-
use diversity), transportation factors (e.g., travel mode, distance, and time), affinity to new
technology, and the institutional settings, would also condition AV adoption rates.
4.2. Policy Recommendations
Since AVs are not yet available for public use, and since many people have very limited
knowledge about AVs, the overall acceptance of AVs is convoluted. Thus, researchers are
formulating alternative strategies to increase public acceptance and adoption of AVs. This
subsection discusses some recommendations for policy makers, auto industries, and private
stakeholders to formulate pertinent policies and strategies in order to encourage people to
use AVs and increase market penetration.
(1)
Broadcasting benefits, features, and usability of AVs on public media (e.g., TV, radio)
and social media can inform and educate people and substantially boost public
acceptance by increasing familiarity with this novel technology [88–90].
(2)
Policy makers, manufacturers, and transport operators can arrange hands-on test
drive opportunities for the people to engage and interact with the technology [
88
–
92
].
Sustainability 2023,15, 11541 22 of 29
This can increase public acceptance of AVs by enhancing the familiarity, trust, and
effort expectancy of AVs and reducing misconceptions of safety barriers.
(3)
An efficient and transparent administration comprising officials from industry and gov-
ernment sectors can facilitate this inevitable transformation in the automotive industry
by allocating subsidies for initial launching, for providing a supportive environment,
and for integrating with existing transport infrastructure and design [
89
,
93
,
94
]. By
doing so, they can significantly increase AV adoption and use.
(4) Concerned authorities can appoint an independent and certified tester to test maturity
standards of AVs and AV producers and set some baseline standards to be maintained
in order to achieve trust in AVs and increase their performance [92].
(5)
As elderly people are reluctant to drive AVs, appropriate actions should be taken to
change their perceptions and boost overall acceptance by increasing trust in AVs and
foregrounding features and benefits of AVs [83,94,95].
(6)
Transportation engineers and designers should simplify the design and positioning
of SAVs by providing clear video instructions, internet cafes, conference rooms, and
social networking places to engage all people and make their journey fun and en-
joyable [
90
,
94
]. Consequently, this can improve user experience and increase public
acceptance and use of SAVs.
(7)
Auto manufacturers and interested stakeholders should invest more and strengthen
research and development of this evolving technology in order to constantly improve
the reliability of the technology and increase people’s trust in order to enhance public
acceptance of AVs [90].
(8)
Practitioners should establish a set of comprehensive mitigation strategies, such as
limiting personal data acquisition, anonymizing users’ identities before sharing data,
instituting strict regulatory frameworks in cyberspace to safeguard consumer data
from cyber-attacks, and alleviate cyber worries [
95
]. This can increase the acceptance
and use of AVs by all cohorts of the society.
(9)
AV manufacturers should be accountable, ease users’ ethical concerns (e.g., privacy,
cybersecurity, human rights), and prepare liability rules involving AVs, human drivers,
and other road users before introducing AVs to the market [
96
]. This intervention
can increase the social welfare of AVs and, thereby, encourage people to adopt and
use AVs.
4.3. Directions for Future Research
The body of research synthesized in this article has contributed solid evidence-based
knowledge on the socio-technical interface of AVs and populations of potential users with
regard to their willingness to adopt this complex set of cutting-edge technologies. It is ex-
pected that this study will inform transportation planners and policy makers with advanced
knowledge on AVs and SAVs, so they can create appropriate policies even before AVs are
fully deployed on the roads [
80
]. The results from these empirical studies could assist
policy makers in identifying the segments of people who will be early adopters of this novel
technology [
82
]. Additionally, the synthesis of study results from developed countries will
provide significant insights to the adoption of AVs and SAVs in less developed countries,
where it is a new concept and where very limited information is available on peoples’
perceptions and acceptance of AVs [
97
,
98
]. Considering the diversity of preferences that
reflect people’s socioeconomic status, the results articulated in this study can suitably assist
producers, government institutions, and other stakeholders in segmentation, targeting, and
promoting AVs [
99
]. Yet, because of the transformative nature of these technologies and
because much of them remain to be commercially available, much remains to be uncovered
at this interface. By analyzing the findings and methodologies of previous studies, we
have identified some limitations and some gaps that may guide future research in this
area. We believe the following aspects articulate a compelling research agenda on people’s
perceptions, opinions, and preferences on AVs.
Sustainability 2023,15, 11541 23 of 29
(1)
Some studies selected samples from a specific stratum (e.g., higher educated people,
experts, tech-savvy, visitors of pilot vehicles, geographically focused samples), and
thus overlooked large segments of the population. In short, data collection may reflect
a self-selection bias and a non-response bias under a controlled environment [
58
,
62
,
68
].
Therefore, large, diverse, and representative segments of people should be included in
the sample to obtain unbiased, true, and insightful results [
35
,
47
,
67
]. Doing so would
enable reliable inference in a larger population, study diversity in human response to
the innovation of autonomous mobility technologies, and be in a position to address
disparities across population segments, particularly to the extent these disparities
may be exacerbated by artificial intelligence (AI) and information technologies.
(2)
Psychological factors are often inadequately measured in studies [
61
,
65
,
66
], failing to
capture their complete effects on the behavioral intentions to adopt AVs. Thus, it is
recommended to include a more complete range of factors of human psychology to
understand fully their effects on AV adoption. Moreover, given that AV technologies
and the modalities of their deployment are still in flux and that the legal, infrastruc-
tural, and human factors are in the process of adjusting to the subtleties of immersion
in a mobility context shaped by AI, we suggest that researchers survey the same panel
of respondents repeatedly over time in order to be in a position to trace trends in
attitudes and perceptions based on their understanding from peers, relatives, social
and digital media, real-life experience of AVs, availability of cutting-edge technol-
ogy, sense of personal risks, and changes in household locations (i.e., rural versus
urban) [
69
]. This would also enable a more direct assessment of causal pathways
and also deepen our understanding of socio-technological systems for designing
and adopting AVs [
100
]. In turn, this would support the design of time-sensitive
information sharing on the opportunities presented by AVs and better policies on AV
deployment that mitigate risks, uncertainties, and disparities.
(3)
By keeping the questionnaire and other survey instruments short and simple, a num-
ber of important questions have often been omitted (also reflected in Figure 3) that
could significantly shed light on people’s perceptions. Thus, the effects on willingness
to adopt and willingness to pay should be investigated considering different costs, ur-
ban form, traffic scenarios, technological advancement and uncertainty in technology,
and institutional settings [
65
,
68
,
101
]. Moreover, productivity, efficiency, and all types
of impacts of AVs should be considered in order to estimate consumer psychology
and intention to adopt AVs [18,68].
(4)
As full-scale AVs are not yet commercially available, most studies collected data
based on the imaginations of travelers, assuming hypothetical driving and urban
settings (i.e., a typical road segment, same speed, homogeneous traffic scenario), and
educating respondents about AVs beforehand, which may be at variance from the
real-world scenario and could influence perceptions [
26
,
71
,
102
]. Moreover, some
studies also generated synthetic data using driving simulators where participants
just sit behind the wheel without doing any direct maneuvering, which does not
capture a real representation of the population [
35
,
50
,
66
]. Thus, further studies should
consider mixed methods integrating simulation and statistical analysis and relevant
user-behavior data reflecting real-world urban environments and traffic scenarios (e.g.,
mixed traffic), which can provide a higher level of accuracy in assessing perceptions
and opinions of people on AVs [44,62,103].
(5) Major legal and ethical aspects (e.g., the requirement of a driving license, responsibility
for crashes involving AVs, whether to sacrifice one to save more, fair access to AV
services for all, etc.) are largely unexplored in the extant literature, which could affect
implementation of AVs [
100
,
104
,
105
]. Thus, future studies should investigate different
legal and ethical values in socio-political, spatial, environmental, and technological
dimensions in order to facilitate future AV adoption.
(6)
Given the number of existing studies on AVs and the conflicting nature of the results
of some of these studies, a systematic econometric meta-analysis would highlight the
Sustainability 2023,15, 11541 24 of 29
consistencies embedded in this body of literature so as to generalize the results of
individual studies and tailor more robust public policies and business practices for
the successful deployment of AVs. Furthermore, most analytic approaches used to
study data on willingness to adopt AVs are econometric and share stiff distributional
and linearity requirements. Given the complexity of the topic of study, it is our
contention that empirical studies using machine learning and deep-learning-based
techniques would enhance our ability to understand the complex relationship between
different internal and external factors operating at multiple levels and the AV adoption
tendency of people.
(7)
As discussed in Table 1, an increasing number of studies are being conducted in
developed countries, where government, auto industries, and concerned private
stakeholders are financing the testing and implementation of AVs. However, AVs and
SAVs are relatively new concepts in developing countries, and information on people’s
perceptions and opinions on AVs and the factors that influence people’s BI towards
AVs are unavailable [
97
,
98
]. People’s perceptions, attitudes, and determinants of AVs
would be different in developing countries compared to developed countries due
to the differences in their socioeconomic statuses, cultures, and attitudes [
98
]. Thus,
research should be conducted to understand people’s perceptions and internal and
external factors of AVs in developing contexts.
(8)
Considering the deep ripple effects of the recent health crisis due to the COVID-19
pandemic on human mobility [106–110], future research should investigate how this
pandemic may have shifted perceptions and opinions of people with regard to sharing
AVs with others amidst the fear of disease transmission and their mobility behaviors,
and how a more resilient transportation and mobility system can be fostered. Al-
though electrification and automation of vehicles have the potential to reduce energy
consumption and carbon emissions, some researchers are skeptical about the net
energy and emission effects of vehicle automation due to increased travel demand [
6
].
Thus, future studies should investigate how these potential health effects may change
the perception and motivation of people to use these technologies.
5. Conclusions
This state-of-the-art literature review investigated the internal and external factors
that influence people’s AV adoption tendencies. The synopsis of the extant literature
was presented systematically in this paper by developing a conceptual framework. By
analyzing the results and methodologies, we also identified key limitations of previous
studies and gaps in our knowledge base, and provided some directions for future research.
This research would be helpful for policy makers and other stakeholders to take appropriate
policy actions in order to promote zero-emission vehicles, manage transportation demand,
and promote smart and sustainable built environments.
Author Contributions:
M.M.R.: Conceptualization, methodology, software, formal analysis, vi-
sualization, writing—original draft, review and editing; J.-C.T.: conceptualization, methodology,
supervision, writing—original draft, review and editing. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors have no conflict of interest to declare.
Sustainability 2023,15, 11541 25 of 29
Abbreviations
ACC Adaptive Cruise Control
ADAS Advanced Driver-Assistance System
ANOVA Analysis of Variance
AVs Autonomous Vehicles
BI Behavioral Intention
BLM Binary Logit Model
CAVs Connected and Autonomous Vehicles
CBD Central Business District
CLM Conditional Logit Model
CVs Connected Vehicles
DS Descriptive Statistics
EVs Electric Vehicles
FA Factor Analysis
GPS Global Positioning System
HLM Hierarchical Linear Model
ICE Internal Combustion Engine
LKM Logit Kernel Model
LLM Log-Linear Regression
LRM Logistic Regression Model
MDCP Multiple Discrete–Continuous Probit
MIP Mixed-Integer Programming
MLM Mixed Logit Model
MLR Multiple Linear Regression
MNL Multinomial Logit
MNP Multinomial Probit Model
NHTSA National Highway Traffic Safety Administration
OLR Ordered Logistic Regression
OPM Ordered Probit Model
PBC Perceived Behavioral Control
PC Pearson Correlation
PEU Perceived Ease of Use
PM Probit Model
PR Perceived Risk
PRPLM Parametric Random Parameter Logit Model
PS Price Sensitivity
PT Perceived Trust
PU Perceived Usefulness
SAE Society of Automotive Engineers
SAVs Shared Autonomous Vehicles
SEM Structural Equation Model
SI Social Influence
SOV Single Occupancy Vehicle
SRPLM Semiparametric Random Parameter Logit Model
SUM Seemingly Unrelated Model
TA Technology Anxiety
TAM Technology Acceptance Model
TPB Theory of Planned Behavior
TRA Theory of Reasoned Action
TS Traffic Safety
VMT Vehicle Miles Traveled
WMNL Weighted Multinomial Logit Model
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