Adoption Of Autonomous Vehicles Research Articles

An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress.

The Environmental Sustainability Potential of Autonomous Vehicles: An Overview

The optimization of transportation systems and the integration of autonomous vehicles (AV) are significantly transforming urban mobility and exerting outstanding effects from an environmental perspective. This article examines the possibilities of autonomous vehicles in reducing traffic congestion, emissions, and energy consumption. The optimized driving style of AVs, dynamic route planning, and enhanced intersection systems have a profound impact on emission reduction. The article also delves into current development trends and challenges, encompassing advancements in AV sensing technologies, traffic safety, and cybersecurity. These findings collectively suggest that the deployment of autonomous vehicles brings substantial benefits to sustainable urban mobility; however, further development is necessary to support the widespread adoption of AVs and strengthen societal trust.

Autonomous Vehicles Perception, Acceptance, and Future Prospects in the GCC: An Analysis Using the UTAUT-Based Model

The emergence of Autonomous Vehicles (AVs) marks a significant advancement in the automotive industry, transitioning from driver-assistance technologies to fully autonomous systems. This change is particularly impactful in the Gulf Cooperation Council (GCC) region, which is a significant automotive market and technological hub. However, the adoption of AVs in the GCC faces unique challenges due to the influence of cultural norms and geographical characteristics. Our research utilizes a customized framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), which is adapted to include cultural and geographical factors. This approach fills a gap in the existing literature by identifying and analyzing the key factors affecting the adoption of AVs in the GCC. Our findings indicate a difference in the receptiveness towards AVs among different demographics. Younger participants displayed a more favorable attitude towards AVs compared to older individuals. Additionally, gender and educational attainment play significant roles in the acceptance of AVs. Specifically, our results suggest that there are variations in acceptance rates among genders and individuals with varying levels of education. The United Arab Emirates (UAE) has a relatively high acceptance rate of AVs due to its advanced infrastructure and openness to technological innovations. Our study identifies facilitating conditions and performance expectancy as crucial determinants of intention to use AVs in the GCC. It emphasizes the importance of infrastructure readiness and the perceived advantages of AVs in promoting their adoption.

Early Perspectives: Exploring the Potential Impacts of Autonomous Vehicles Through the Lens of Urban Mobility and Urban Form

The emergence of autonomous vehicles (AVs) is expected to significantly reshape urban mobility and travel behavior patterns. AVs have the potential to offer higher levels of convenience, safety, and accessibility while enabling users to spend their trip time on more efficient and productive tasks such as working or even relaxing. This transformation in the short- to mid-term could result in changes in the public's sensitivity and perception toward enduring longer travel times and consequently, in mid- to long-term, it could influence the willingness to reconsider their residential locations. Therefore, the objective of this study is to enhance the understanding of the potential effects of AVs on travel behavior and land use through the examination of stated preference queries. To achieve this objective, various multinomial logit models toward AVs adoption and residential relocation were estimated by employing a dataset from Istanbul, Türkiye, as an example of megacity in a developing country. While the study findings revealed a set of potential adoption barriers for AVs, they also indicated a notable propensity for adopting these vehicles. Furthermore, concerning individuals' willingness to reconsider their residential locations due to the emergence of AVs, a considerable positive attitude was evident, albeit guarded. This attitude suggests that individuals' decisions are still evolving and can be guided toward the desired future through well-timed and well-suited policies. The outcomes of the study can serve as valuable input for policymakers as well as transportation and urban planners, offering insights into the potential impacts of AVs on urban mobility and form.

Demographic and social differences in autonomous vehicle technology acceptance in Hungary

Progress in automation has resulted in a growing number of autonomous vehicles (AVs). However, demographic and social differences behind the acceptance of AV technology are an emerging topic in the East-Central European region. These countries (e.g., Poland, Slovakia, Romania) move on a similar technological development path, and the social and economic conditions are alike; thus, Hungary represents this region well. This study contributes to fill this niche. We used quantitative research methods (factor analysis, Kruskal-Wallis H test, Pearson-correlation) to analyze with a snowball (non-probability) sampling. The Hungarian respondents (N=949) selected in the sample were interviewed through a face-to-face and online quantitative questionnaire. The results show that gender and age influence mostly the acceptance; residence and occupation have only partial influence. The propensity to take risks is significantly differentiated in almost all demographic segments. The results facilitate differentiation of users based on their demographic characteristics in AV adoption. Furthermore, the integration of risk propensity into the analysis helps to identify which potential user groups are more likely to overcome any fears of novelty or which clusters are more likely to adopt the current framework of safe transport without driver control. The outcomes are of interest to engineers, manufacturers and policy-makers who can adapt their products, services and taking measures to meet the mobility needs of potential users and introduce effective incentives to increase public acceptance of AVs.

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

LLM-Powered Synthetic Environments for Self-Driving Scenarios

This paper outlines a proposal exploring the potential use of Large Language Models (LLMs), particularly GPT-4, in crafting realistic synthetic environments for self-driving scenarios. The envisioned approach involves dynamic scene generation within game engines, leveraging LLMs to introduce challenging elements for autonomous vehicles. The proposed evaluation process outlines assessments such as realistic testing, safety metrics, and user interaction, aiming to set the stage for potential improvements in self-driving system performance. The paper aims to contribute to the AI field by discussing how LLMs could be utilized to create valuable testing grounds for autonomous vehicles, potentially fostering the development of more robust self-driving technology. The envisioned impact is the eventual enhancement of road safety and the possible acceleration of the adoption of autonomous vehicles, paving the way for a future with safer and more efficient transportation.

Digital twin‐based multi‐objective autonomous vehicle navigation approach as applied in infrastructure construction

AbstractThe widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation, with path planning emerging as a critical aspect. However, existing road infrastructure confronts challenges due to prolonged use and insufficient maintenance. Previous research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance, while neglecting road construction information, leading to potential time and energy inefficiencies in real‐world scenarios involving infrastructure development. To address this issue, a digital twin‐embedded multi‐objective autonomous vehicle navigation is proposed under the condition of infrastructure construction. The authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extraction and modelling of the autonomous vehicle workspace. Additionally, a wavelet neural network is developed to predict real‐time traffic flow, considering its inherent characteristics. Moreover, a multi‐objective brainstorm optimisation (BSO)‐based method for path planning is introduced, which optimises total time‐cost and energy consumption objective functions. To ensure optimal trajectory planning during infrastructure construction, the algorithm incorporates a real‐time updated digital twin throughout autonomous vehicle operations. The effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road construction. The results highlight the improved performance and reliability of the autonomous vehicle system when equipped with the authors’ approach, demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.

Trust and intention to use autonomous vehicles in Bangkok, Thailand

This paper examines the intention to use autonomous vehicles (AVs) in Bangkok, Thailand. The level of readiness for AVs in terms of infrastructure, technology, and regulation in our study area may represent those in other developing countries where individual preferences and operational environment are generally different from more developed countries. We analyzed survey data of 797 respondents in the Bangkok Metropolitan Region, using a modified Technology Acceptance Model. The model incorporated additional factors, including trust, perceived risk, and operational environment of AVs, as well as how they could affect the intention to use AVs. We found that all latent constructs, i.e., perceived usefulness, perceived ease of use, trust, and perceived risk, were significantly associated with the intention to use. Trust was found to be the most powerful factor; it directly increased intention to use the vehicles, and indirectly increased intention to use through improvement of perceived ease of use and reduction in perceived risk. These findings imply that familiarizing the general public with AV-related technologies might help to improve trust and promote AVs adoption. The results also suggested that to promote the use of AVs in developing countries with irregular traffic patterns and mixed road usage, the operating environments in areas where AVs are allowed should be well designed and regulated to ensure confidence of the public.

Driving forward together: The common intention of Indonesians in different residential areas to use autonomous vehicles

Despite the significant benefits of Autonomous Vehicles (AVs) for global transportation, Indonesia's diverse geographical landscape encounter unique adoption challenges due to infrastructural shortcomings and economic limitations. This study explores the adoption of AVs in Indonesia, considering its potential market and crucial role in AV and Electric Vehicle supply chains. Drawing upon the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), we assessed AV acceptance across Indonesia's Metropolitan Cities, frontier regions (“3T”), and the New National Capital City (IKN) areas. Using a cross-sectional study design, we distributed an online questionnaire, focusing on demographics, perceived safety, transport mode changes, and behavioral intention towards AVs, based on TAM and UTAUT factors. From 1,255 valid responses, we found significant influences of gender (t (1253) = 4.22), safety perceptions (F (2,1252) = 52.373), and transport mode frequency (F (4, 1250) = 6.662) on AVs adoption intentions. Both TAM and UTAUT models were moderately effective in explaining willingness to use AVs (R2 = 34% and 48%, respectively). TAM highlighted perceived usefulness (β = 0.421) and ease of use (β = 0.540), while UTAUT emphasized effort expectancy (β = 0.317) and social influence (β = 0.240). However, the findings from the multigroup analysis did not corroborate the role of residential areas in determining the intention to use AVs. These findings offer crucial insights for developing AV promotion strategies, creating user-friendly designs, and formulating supportive policies for AV adoption in diverse Indonesian regions.

A Conceptual Framework for Acceptance of Autonomous Vehicle in Malaysia

Mobility is evolving globally. Automated vehicle technology is consistently advancing with the development of Artificial Intelligence and information communication technology. The Autonomous Vehicle is acknowledged with the benefits of reducing traffic fatalities, reduced emissions and convenience. If autonomous vehicles are widely adopted, the Sustainable Development Goals could be achieved. Various studies have been conducted to investigate the psychological factors (internal) as well as assess the efforts of institutions (external) in promoting the adoption of autonomous vehicles. Nonetheless, there is very few studies examined the impacts of internal and external factors on the acceptance of autonomous vehicles simultaneously. Therefore, this study is taken to close the gap in understanding the acceptance of autonomous vehicles in Malaysia by integrating the internal factors and external factors in a model. By reviewing the past studies, a conceptual framework which can offer a comprehensive insight to the policymakers and car makers from the public’s perspective is proposed. Implications from this study can serve as a basis for prioritising the budget resources and development guidelines for the successful implementation of autonomous vehicles in Malaysia.

THE FUTURE OF AUTONOMOUS VEHICLES IN THE U.S. URBAN LANDSCAPE: A REVIEW: ANALYZING IMPLICATIONS FOR TRAFFIC, URBAN PLANNING, AND THE ENVIRONMENT

This study presents a comprehensive analysis of the impact of autonomous vehicles (AVs) on urban landscapes, focusing on traffic management, urban planning, and environmental sustainability in the United States. The main objectives were to explore the evolution of AV technology, assess its current state and innovations, and understand its implications for urban environments. Employing a systematic literature review and content analysis, the study analyzed data from peer-reviewed academic journals, industry reports, and government publications, focusing on literature published from 2007 to 2023. Key findings indicate that AVs have the potential to significantly alter urban traffic dynamics, reducing congestion and enhancing road safety. In urban planning, AVs necessitate a reevaluation of infrastructure, potentially leading to more efficient land use and reduced parking needs. Environmentally, AVs offer the promise of reduced emissions and improved air quality, especially when integrated with electric vehicle technology. However, these benefits are contingent upon overcoming technological challenges, ensuring public safety, and developing effective regulatory frameworks. The study envisions a future urban landscape where AVs contribute to more efficient, safer, and environmentally sustainable environments. It recommends comprehensive policy and industry strategies for the successful integration of AVs, emphasizing the need for standardized safety regulations, incentivizing AV adoption, and ensuring equitable access. Concluding insights suggest that while AVs hold significant potential for reshaping urban mobility, their successful integration requires addressing technological, policy, and societal challenges. Future research should focus on the long-term impacts of AVs on urban landscapes, exploring ethical and legal implications, and their role in broader sustainability goals.
 Keywords: Autonomous Vehicles, Urban Planning, Traffic Management, Environmental Sustainability.

Autonomous vehicles adoption motivations and tourist pro-environmental behavior: the mediating role of tourists’ green self-image

Purpose This study aims to examine the impact of autonomous vehicles adoption motivations (i.e. technological, ecological and intrinsic motivation) on tourists’ pro-environmental behavior and verify the mediating role of tourists’ green self-image between the relationship of eco-friendly attitudes and autonomous vehicles adoption motivations. Design/methodology/approach The data from 586 national and international tourists were analyzed using the partial least squares method. Findings The findings revealed that eco-friendly attitude is a significant predictor of tourists’ green self-image; tourists’ green self-image is a significant predictor of autonomous vehicles adoption motivations; and autonomous vehicles adoption motivations are significant predictors of tourists’ pro-environmental behavior. In addition, tourists’ green self-image mediated the relationship between eco-friendly attitudes and autonomous vehicles adoption motivations. Originality/value These outcomes provide valuable guidance for the future development of green destination tourism and allow interesting implications for the tourism industry and autonomous vehicles adoption.

Factors Affecting Autonomous Vehicles Adoption: A Systematic Review, Proposed Framework, and Future Roadmap

Autonomous vehicles (AVs) offer several benefits, such as improving road safety, mitigating traffic congestion, and reducing fuel consumption and gas emissions. Despite these benefits, their adoption rate remains limited due to various factors influencing users’ decisions. While previous studies have identified numerous factors influencing AV adoption using various adoption frameworks, the factors have not been comprehensively analyzed and synthesized. Thus, this systematic review aims to bridge this gap by identifying and classifying the factors influencing the adoption of AVs. Out of 3,532 collected research papers, 71 empirical studies were analyzed thoroughly. The findings demonstrated that the technology acceptance model (TAM) was the most widely used model for investigating AV adoption. The identified factors in the analyzed studies were classified into distinct categories: psychological and behavioral factors, technological factors, social factors, environmental factors, security and privacy factors, AV-related factors, risky and negative factors, conditional factors, and monetary factors. We have proposed an AV adoption framework grounded in this taxonomy to direct subsequent empirical research. We have also highlighted numerous agendas to serve as a blueprint for future AV adoption studies. This review offers various theoretical insights and actionable recommendations for multiple AV research, development, and implementation stakeholders.

Psychological factors shaping public acceptance of the adoption of autonomous vehicles in Indonesia

IntroductionThe rapid development of autonomous vehicles (AVs) has the potential to revolutionize transportation systems and address societal challenges. However, understanding the psychological factors influencing public acceptance and readiness for AV adoption is crucial for successful implementation. This study provides a comprehensive analysis of these psychological factors, specifically focusing on the context of health issues in Indonesia. MethodologyA mixed-methods approach analyzed quantitative data from 500 respondents to a questionnaire addressing key perceptions of AVs using regression analysis, coupled with 30 qualitative interviews. ResultsThe results revealed that trust in AV technology mediates the relationship between control perception, perceived safety, human-machine interaction, educational status, cultural factors, and public acceptance of AV adoption. Cultural factors were also identified as moderators influencing this relationship. Qualitative analysis of interview data further highlighted the significance of trust, reliability, and safety concerns in shaping public acceptance. Participants emphasized the need for effective human-machine interaction and educational awareness to enhance understanding and acceptance. DiscussionThis research contributes to the existing literature by explicitly examining psychological factors within the context of health issues in Indonesia, providing valuable insights for policymakers aiming to promote AV adoption. Ultimately, understanding and addressing these psychological factors will pave the way for a successful and sustainable AV future.

The effect of technological readiness dimensions on the adoption of autonomous vehicles: Focusing on behavioral reasoning theory

The main goal of this study was to investigate the impact of four dimensions of technological readiness on the adoption of autonomous vehicles (AVs): Discomfort, insecurity, optimism, and innovativeness. This study confirmed that reasons for (against) adoption of technology are also important determinants of the intention to use AVs with the moderating effect of social influence on technological readiness and reasons for (against) adoption AVs are also investigated. Thus, this research adds to the existing literature on AVs adoption by establishing the important roles of consumers’ technological readiness and social influence through their behavioral reasoning processes. For business practitioners and marketers, the findings provide guidelines for developing marketing strategies that fit an individual customers’ level of adoption.

An analysis of physiological responses as indicators of driver takeover readiness in conditionally automated driving

By the year 2045, it is projected that Autonomous Vehicles (AVs) will make up half of the new vehicle market. Successful adoption of AVs can reduce drivers’ stress and fatigue, curb traffic congestion, and improve safety, mobility, and economic efficiency. Due to the limited intelligence in relevant technologies, human-in-the-loop modalities are still necessary to ensure the safety of AVs at current or near future stages, because the vehicles may not be able to handle all emergencies. Therefore, it is important to know the takeover readiness of the drivers to ensure the takeover quality and avoid any potential accidents. To achieve this, a comprehensive understanding of the drivers’ physiological states is crucial. However, there is a lack of systematic analysis of the correlation between different human physiological responses and takeover behaviors which could serve as important references for future studies to determine the types of data to use. This paper provides a comprehensive analysis of the effects of takeover behaviors on the common physiological indicators. A program for conditional automation was developed based on a game engine and applied to a driving simulator. The experiment incorporated three types of secondary tasks, three takeover events, and two traffic densities. Brain signals, Skin Conductance Level (SCL), and Heart Rate (HR) of the participants were collected while they were performing the driving simulations. The Frontal Asymmetry Index (FAI) (as an indicator of engagement) and Mental Workload (MWL) were calculated from the brain signals to indicate the mental states of the participants. The results revealed that the FAI of the drivers would slightly decrease after the takeover alerts were issued when they were doing secondary tasks prior to the takeover activities, and the higher difficulty of the secondary tasks could lead to lower overall FAI during the takeover periods. In contrast, The MWL and SCL increased during the takeover periods. The HR also increased rapidly at the beginning of the takeover period but dropped back to a normal level quickly. It was found that a fake takeover alert would lead to lower overall HR, slower increase, and lower peak of SCL during the takeover periods. Moreover, the higher traffic density scenarios were associated with higher MWL, and a more difficult secondary task would lead to higher MWL and HR during the takeover activities. A preliminary discussion of the correlation between the physiological data, takeover scenario, and vehicle data (that relevant to takeover readiness) was then conducted, revealing that although takeover event, SCL, and HR had slightly higher correlations with the maximum acceleration and reaction time, none of them dominated the takeover readiness. In addition, the analysis of the data across different participants was conducted, which emphasized the importance of considering standardization or normalization of the data when they were further used as input features for estimating takeover readiness. Overall, the results presented in this paper offer profound insights into the patterns of physiological data changes during takeover periods. These findings can be used as benchmarks for utilizing these variables as indicators of takeover preparedness and performance in future research endeavors.

Promoting Veteran-Centric Transportation Options through Exposure to Autonomous Shuttles

Veterans face difficulties accessing vital health and community services, especially in rural areas. Autonomous vehicles (AVs) can revolutionize transportation by enhancing access, safety, and efficiency. Yet, there is limited knowledge about how Veterans perceive AVs. This study fills this gap by assessing Veterans’ AV perceptions before and after exposure to an autonomous shuttle (AS). Using a multi-method approach, 23 participants completed pre- and post-AS Autonomous Vehicle User Perception Survey (AVUPS), with 10 participants also taking part in post-AS focus groups. Following exposure to the AS, differences were observed for three out of the four AVUPS domains: an increase in Intention to Use (p < 0.01), a decrease in Perceived Barriers (p < 0.05), and an increase in Total Acceptance (p = 0.01); Well-being remained unchanged (p = 0.81). Feedback from focus groups uncovered six qualitative themes: Perceived Benefits (n = 70), Safety (n = 66), Shuttle Experience (n = 47), AV Adoption (n = 44), Experience with AVs (n = 17), and Perception Change (n = 10). This study underscores AVs’ potential to alleviate transportation challenges faced by Veterans, contributing to more inclusive transportation solutions. The research offers insights for future policies and interventions aimed at integrating AV technology into the transportation system, particularly for mobility-vulnerable Veterans in rural and urban settings.

Street re-design for the adoption of autonomous vehicles: case of the city of Doha, Qatar

PurposeThere is lack of knowledge about how the existing streets need to be redesigned and the infrastructural changes that need to be made to adopt autonomous vehicles. The purpose of this study is to investigate the infrastructure requirements of autonomous vehicles in terms of (1) lane widths, (2) parking spaces, (3) drop-off zones and (4) other facilities, followed by analyzing them and suggesting changes in the existing urban design of Msheireb Downtown Doha (MDD).Design/methodology/approachMixed method of combining both qualitative (secondary research of analyzing the existing data about the urban design guidelines for an autonomous future, observations of the existing infrastructure) and quantitative methods (on-site measurements of pedestrian walkways and road lane widths) is used.FindingsThe outcome of the research consists of a series of major infrastructural changes with regard to lane widths, parking spaces, pick-up and drop-off zones and other facilities needed for the deployment of autonomous vehicles.Practical implicationsThe results imply that Qatar can benefit by adopting the proposed urban design suggestions for the implementation of autonomous vehicles on the streets of MDD in particular, and smart cities of Qatar and the region in general.Social implicationsThe proposed changes can work as a reference and serve as a possible setting for addressing Autonomous Vehicle preparations in emerging cities.Originality/valueThe proposed urban design changes can be adapted for an autonomous future in emerging cities.

Autonomous vehicles: What are your intentions?

Autonomous vehicles provide a variety of societal and environmental benefits, however significant technological, legislative, and infrastructural advancements are still required for successful integration of autonomous vehicles into society. The present study applied the Theory of Planned Behaviour to examine the factors driving and impeding autonomous vehicle adoption. Participants were 254 adults (Mage = 37.89, SD = 14.33) who took part in an online survey assessing attitudes, subjective norms, perceived behavioural control, perceived barriers and intentions to adopt autonomous vehicles. Results indicated that attitudes and both personal and work-related subjective norms were positively associated with adoption intention. An association was not found between perceived behavioural control and adoption intention, and the relationships between psychosocial factors and adoption intention were not found to be moderated by perceived barriers. Concerns regarding the risk of operating errors and fear of equipment or system failure were found to be negatively associated with adoption intention and concerns regarding cost was found to be positively associated with adoption intention. Encouragingly from a safety perspective, it appears that concerns regarding safety are more influential to adoption intention than costs associated with the purchase of the vehicle. The results of this research have important implications for future research and integration of autonomous vehicles into society.