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

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered... 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/approachThe 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.FindingsThe 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/valueThe results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent and Connected Vehicles Emerald Publishing

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

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

Yuan, Quan; Xu, Xuecai; Wang, Tao; Chen, Yuzhi
Journal of Intelligent and Connected Vehicles , Volume 5 (3): 7 – Oct 11, 2022

Abstract

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/approachThe 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.FindingsThe 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/valueThe results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

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Publisher
Emerald Publishing
Copyright
© Quan Yuan, Xuecai Xu, Tao Wang and Yuzhi Chen.
ISSN
2399-9802
DOI
10.1108/jicv-04-2022-0012
Publisher site
See Article on Publisher Site

Abstract

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/approachThe 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.FindingsThe 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/valueThe results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

Journal

Journal of Intelligent and Connected Vehicles – Emerald Publishing

Published: Oct 11, 2022

Keywords: Safety; Bayesian random parameter ordered probit model; Liability; Autonomous vehicles; Advanced vehicle safety systems

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