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Research paper | Open Access

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

Quan Yuan1,2, Xuecai Xu3( ), Tao Wang4, Yuzhi Chen4
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China
Center for Intelligent Connected Vehicles and Transportation, Tsinghua University, Beijing, China
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China
School of Architecture and Transportation, Guilin University of Electronic Technology, Guilin, China

This paper forms part of a special section “Road Safety in the Era of Intelligent & Connected Vehicles”, guest edited by Yanyong Guo.

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Abstract

Purpose

This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.

Design/methodology/approach

The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously.

Findings

The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.

Originality/value

The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

Keywords

SafetyLiabilityAutonomous vehiclesBayesian random parameter ordered probit modelAdvanced vehicle safety systems

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Journal of Intelligent and Connected Vehicles
Volume 5 Issue 3,
October 2022
Pages 199-205
DOI: 10.1108/JICV-04-2022-0012
Cite this article:
Yuan Q, Xu X, Wang T, et al. Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis. Journal of Intelligent and Connected Vehicles, 2022, 5(3): 199-205. https://doi.org/10.1108/JICV-04-2022-0012

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Received: 22 April 2022
Revised: 01 June 2022
Accepted: 15 June 2022
Published: 30 June 2022
© 2022 Quan Yuan, Xuecai Xu, Tao Wang and Yuzhi Chen. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode
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