Abstract

This study aimed to identify critical factors that influence acceptance of automated vehicles among drivers. The focus of this study was on automated vehicles (AVs) of level 3 (conditional driving automation) as defined by the Society of Automotive Engineers. A research model was proposed here by using the technology acceptance model (TAM) with trust, risk perception (perceived safety risk and perceived privacy risk), compatibility, and system quality. A cross-sectional structured questionnaire survey was used to collect quantitative data from 237 drivers in Hong Kong. The data were analyzed to test the proposed research model by structural equation modeling. The proposed research model was found to explain 68% of the variance in intention to use AVs. In contrast with the TAM constructs of perceived usefulness and perceived ease of use, the results of this study indicated that trust was the most important factor in shaping a positive attitude towards using AVs, which affected driver intention to use AVs. Also, trust was found to be influenced by perceived safety risk, compatibility, and system quality. This study is the first attempt to consider technological factors related to AVs (compatibility and system quality) in explaining AV acceptance among drivers and highlighted the importance of the technological factors in the context of driver acceptance of AVs. Based on the findings of this study, several recommendations are discussed to help AV developers and governments to improve driver attitudes towards adoption of AVs.

Highlights

  • Vehicle automation is one of the most fundamental applications in the field of intelligent transportation systems

  • This study extended technology acceptance model (TAM) by involving trust, risk perception, compatibility, and system quality to examine the causal determinants of driver intention to use an Automated vehicles (AVs) in Hong Kong

  • This attitude is shaped by perceived usefulness, perceived ease of use, and trust which are influenced by perceived safety risk, perceived privacy risk, compatibility, and system quality

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Summary

Introduction

Vehicle automation is one of the most fundamental applications in the field of intelligent transportation systems. With new in-vehicle technologies such as adaptive cruise control and global positioning systems incorporated into modern automobiles, the control of driving tasks may shift from a human driver to a machine system [1]. Automated vehicles (AVs) are defined as vehicles capable of navigating, controlling, and avoiding hazards partly or totally without human intervention. According to the Society of Automotive Engineering [2], AVs can be categorized into six levels, ranging from no automation (SAE level 0) to full automation (SAE level 5). Vehicles in SAE levels 0-2 require driver support and supervision with human intervention when necessary. Vehicles in SAE levels 3-5 are able to sense their surrounding environment and take autonomous appropriate

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