The Twofold Role of Legal Liability Misattribution on Intention to Buy Automated Vehicles: A Survey in China

Abstract

Liability attribution for crashes involving automated vehicles (AVs), if applied improperly, is a factor which can potentially hinder acceptance. The present study investigated the impact of liability attribution on intention to buy an AV. A vignette-based survey was implemented with a hypothetical crash similar to the 2018 Uber crash (which was jointly caused by driver distraction and the malfunctioning of the automated system) leading to a pedestrian’s fatality. Respondents (N = 1524) chose their preferred liability attribution, ranging from human driver exclusively liable to AV manufacturer exclusively liable. Respondents were then randomly allocated to different conditions of actual liability attribution by the local authority. These conditions were then combined into, negative misattribution (the authority assigned more liability to the human driver, compared to the respondent), positive misattribution (the authority assigned less liability to the human driver), and no misattribution. Negative misattribution negatively affected intention to buy; however, positive misattribution did not have a significant impact. The results of a multiple-mediator model indicated that negative misattribution affects intention to buy through the mediating effects of trust, negative affect, and crash acceptability. Theoretical and practical implications of our results are discussed.

Keywords:

• Automated vehicles
• liability attribution
• acceptance
• trust
• negative affect

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this study can be found online at: https://osf.io/37fsb/?view_only=acc84c3cf7d34c46991417c3dd197c9c.

Additional information

Funding

This research was jointly supported by the EU-funded PAsCAL project [grant agreement No 815098, https://www.pascal-project.eu/] aimed at enhancing driver behaviour and public acceptance of connected and automated vehicles, and by the Foreign Expert Project: G2022022004L which enables international collaboration in intelligent transport systems, clean air, and connected mobility.

Notes on contributors

Evangelos Paschalidis

Evangelos Paschalidis is a Research Fellow at the Institute for Transport Studies, University of Leeds, UK. He received his PhD in 2019 at the same department. His main research interests are in the areas of driving behaviour modelling, choice modelling, human factors, traffic psychology and automated vehicles.

Siming Zhai

Siming Zhai is with the Center for Psychological Sciences, Zhejiang University, China. Her research is on human factors and social sciences relevant to automated vehicles. Her work has been published in Accident Analysis & Prevention.

Junhua Guo

Junhua Guo is a Professor at East China Jiaotong University (ECJTU) and the head of the School of Transportation Engineering. He is the executive director of the China Society of Logistics and an information consultant of the General Office of the Jiangxi Provincial Party Committee. His research focuses on intelligent transportation systems and logistical optimization.

Tangjian Wei

Tangjian Wei is currently an Associate Professor with the School of Transportation Engineering, East China Jiaotong University, China, and a visiting researcher with the Institute for Transport Studies, the University of Leeds, UK. His research focuses on railway demand estimation, railway passenger travel choice, and the optimization method applied in transport systems.

Peng Liu

Peng Liu is with the Center for Psychological Sciences, Zhejiang University, China. His research is focused on human factors and ergonomics, risk analysis, and risk perception related to complex systems such as automated driving and nuclear power plants.

Haibo Chen

Haibo Chen is a Professor in Intelligent Transport Systems at the University of Leeds, and the Leader of the Spatial Modelling and Dynamics group. His work involves the development of innovative solutions to reduce congestion and environmental impacts, and more recently, optimisation of fuel consumption, and automated road transport systems.