School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China.
School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; National Engineering Laboratory for Integrated Optimization of Road Traffic and Safety Analysis Technologies, 88 Qianrong Rd, Wuxi 214151, China.
Accid Anal Prev. 2024 Feb;195:107383. doi: 10.1016/j.aap.2023.107383. Epub 2023 Nov 18.
Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV's automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV's operational design domain and the development and optimization of the AV's ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations.
交叉口是自动驾驶车辆（AV）的高风险区域。基于预碰撞场景的碰撞致因分析可以为这些碰撞提供新的见解，从而导致有效的对策，但自动驾驶车辆和传统车辆的预碰撞场景存在显著差异，并且自动驾驶车辆数据不足限制了研究。然而，关联规则方法可以在这些限制下产生有用的结果。因此，本研究旨在使用该方法对预碰撞场景进行分析，以从最新的 5 年 AV 碰撞数据中了解交叉口 AV 碰撞的特点和促成因素。对 197 起交叉口 AV 碰撞的分析揭示了 30 种预碰撞场景。AV 最常发生的碰撞场景是追尾碰撞（58.88%）和变道碰撞（16.24%）。传统车辆追尾 AV 的比例为 58.38%。通过关联规则确定了这两个最常见的 AV 场景的主要促成因素，并从 AV 决策的角度分析了碰撞原因。这两个最常见的 AV 场景的主要促成因素是交叉口相关区域内交叉口外的位置、交通信号控制、自动驾驶模式、混合或公共土地以及工作日，而变道场景的主要促成因素是路边停车和上午 8 点的时间。导致 AV 追尾场景的主要原因是 AV 的自动驾驶系统（ADS）的停止和减速决策不足，以及在变道碰撞中避免碰撞的决策不足。对预碰撞特征和促成因素的识别为 AV 碰撞致因提供了新的见解，并可用于确定 AV 的运行设计域以及在交叉口开发和优化 AV 的 ADS。这些发现还可以在指导交通安全机构发现 AV 热点和提出 AV 管理法规方面发挥作用。
