The Social Acceptance of Autonomous Vehicles

Kurniawan, Jude Herijadi; Chng, Samuel; Cheah, Lynette

doi:10.1109/mpot.2020.2991059

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…The public perception of AI also presents a fundamental obstacle to the widespread adoption of ICPSs. Kurniawan et al [38] argue that research on the social implications of autonomous vehicles is insufficiently developed. Understanding and shaping the social acceptance of ITSs can therefore be achieved by identifying the social driving forces behind autonomous vehicles.…”

Section: Cyber-physical Challenges and Technical Barriersmentioning

confidence: 99%

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

2023

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Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems.

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Section: Cyber-physical Challenges and Technical Barriersmentioning

confidence: 99%

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

2023

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“…Understanding consumer preferences and perceptions is vital for enhancing road safety, travel experience, and traffic efficiency. While previous studies have shown a positive general opinion towards highly automated vehicles, they often overlook behavioral intentions [14][15][16][17]. Factors such as driving enjoyment, environmental concerns, and proautonomous vehicle attitudes play significant roles in consumers' choice between regular cars, privately-owned autonomous vehicles, and shared-autonomous vehicles [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

On Modelling and Investigating User Acceptance of Highly Automated Passenger Vehicles

Panagiotopoulos,

Dimitrakopoulos,

Keraite

2024

IEEE Open J. Intell. Transp. Syst.

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Highly automated passenger vehicles hold great potential to alleviate traffic congestion, enhance road safety, and revolutionize the travel journey. However, while much attention has been given to the technical aspects of this technology, the investigation of public acceptance remains crucial for successful implementation in the global market. To address this gap, this paper introduces innovative research that explores the predictors influencing consumers' intention to adopt highly automated passenger vehicles. Through an online questionnaire-based survey conducted among European adults, we extend the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to incorporate three additional constructs: perceived reliability/trust, perceived financial cost, and perceived driving enjoyment. The key findings of this study underscore the significance of driving enjoyment, financial cost, social influences, and reliability/trust as influential predictors of consumers' intention to adopt highly automated passenger vehicles. By considering these factors, automotive stakeholders can gain valuable insights to develop effective strategies and approaches for the successful implementation of highly automated passenger vehicles in the near future. Last, its innovations pave the way for a transformative shift in transportation, enabling the realization of safer, more efficient, and enjoyable travel experiences for individuals and society as a whole.

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“…Autonomous vehicles (AVs) have garnered substantial attention from various sectors, including government agencies and policymakers, the automotive and technology industries, and academia, due to their potential to enhance road safety, improve travel efficiency, and reduce energy consumption [1][2][3][4]. In several countries, such as China, Japan, the United Kingdom, Sweden, Germany, and Japan, AVs have been permitted or are in the process of being permitted for deployment in specific locations such as experimental test tracks, academic campuses, and demarcated urban zones, with restrictions to avoid mass public exposure [5][6][7]. In the U.S., in 2021, President Biden signed the USD 1 trillion Infrastructure Investment and Jobs Act (IIJA), which commits $110 billion to roads, bridges, and other significant projects, and mandated the United States Department of Transportation (USDOT) to develop the Automated Vehicles Comprehensive Plan (AVCP) to ensure American leadership in autonomous vehicle technologies [8].…”

Section: Introductionmentioning

confidence: 99%

A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure

et al. 2023

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To standardize definitions and guide the design, regulation, and policy related to automated transportation, the Society of Automotive Engineers (SAE) has established a taxonomy consisting of six levels of vehicle automation. The SAE taxonomy defines each level based on the capabilities of the automated system. It does not fully consider the infrastructure support required for each level. This can be considered a critical gap in the practice because the existing taxonomy does not account for the fact that the operational design domain (ODD) of any system must describe the specific conditions, including infrastructure, under which the system can function. In this paper, we argue that the ambient road infrastructure plays a critical role in characterizing the capabilities of autonomous vehicles (AVs) including mapping, perception, and motion planning, and therefore, the current taxonomy needs enhancement. To throw more light and stimulate discussion on this issue, this paper reviews, analyzes, and proposes a supplement to the existing SAE levels of automation from a road infrastructure perspective, considering the infrastructure support required for automated driving at each level of automation. Specifically, we focus on Level 4 because it is expected to be the most likely level of automation that will be deployed soon. Through an analysis of driving scenarios and state-of-the-art infrastructure technologies, we propose five sub-levels for Level 4 automated driving systems: Level 4-A (Dedicated Guideway Level), Level 4-B (Expressway Level), Level 4-C (Well-Structured Road Level), Level 4-D (Limited-Structured road Level), and Level 4-E (Disorganized Area Level). These sublevels reflect a progression from highly structured environments with robust infrastructure support to less structured environments with limited or no infrastructure support. The proposed supplement to the SAE taxonomy is expected to benefit both potential AV consumers and manufacturers through defining clear expectations of AV performance in different environments and infrastructure settings. In addition, transportation agencies may gain insights from this research towards their planning regarding future infrastructure improvements needed to support the emerging era of driving automation.

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The Social Acceptance of Autonomous Vehicles

Cited by 10 publications

References 4 publications

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

On Modelling and Investigating User Acceptance of Highly Automated Passenger Vehicles

A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure

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