期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Investigating safety and liability of autonomous vehicles:Bayesian random parameter ordered probit model analysis 被引量:3
1
作者 Quan Yuan Xuecai Xu +1 位作者 Tao Wang Yuzhi Chen 《Journal of Intelligent and Connected Vehicles》 EI 2022年第3期199-205,共7页
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.Desi... 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. 展开更多
关键词 SAFETY Bayesian random parameter ordered probit model LIABILITY Autonomous vehicles Advanced vehicle safety systems
在线阅读 下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部