交叉口自动驾驶车辆事故前场景生成与致因分析  

Pre-crash Scenario Generation and Causation Analysis for Autonomous Vehicle Crashes at Intersections

作　　者：刘倩 王雪松[1,2] LIU Qian;WANG Xue-song(College of Transportation Engineering,Tongji University,Shanghai 201804,China;The Key Laboratory of Road and Traffic Engineering of Ministry of Education,Tongji University,Shanghai 201804,China)

机构地区：[1]同济大学交通运输工程学院,上海201804 [2]同济大学道路与交通工程教育部重点实验室,上海201804

出　　处：《中国公路学报》2024年第4期297-309,共13页China Journal of Highway and Transport

基　　金：国家自然科学基金项目(52372335);上海市“科技创新行动计划”“一带一路”国际合作项目(23210750500)。

摘　　要：自动驾驶技术在混合交通环境中仍面临诸多安全挑战,交叉口是自动驾驶车辆事故的高发地,为了解决交叉口自动驾驶车辆事故前场景生成和事故致因分析问题,构建了“道路-交通参与者-关键事件-事故前运动状态”的事故前场景方法。基于加州470份自动驾驶车辆事故报告,生成了交叉口31类自动驾驶车辆事故前场景,基于统计方法确定了自动驾驶车辆与传统车辆事故前场景的显著差异。提出了基于系统控制结构的事故致因方法,解析了自动驾驶车辆事故与道路、交通情况、环境、自动驾驶系统、驾驶人(测试员)、车辆的交互关系,分别识别了追尾和换道场景的九类事故致因类型和致因链。研究结果表明:自动驾驶车辆被传统车辆追尾的比例是传统车辆追尾的4.03倍。追尾事故的主要原因是传统车辆跟车过近、自动驾驶系统减速停止或启动决策不充分;换道事故的主要原因是传统车辆危险换道或超车、自动驾驶系统对他车换道意图识别和减速避撞决策不充分。该研究推动了基于场景的事故致因方法的应用,研究结果可以指导自动驾驶测试场景的构建,并为自动驾驶系统开发优化和交叉口安全改善提供参考。The automated driving technology still faces many safety challenges in mixed traffic environments.Intersections are high-risk locations for autonomous vehicles(AVs).The aims of this study are pre-crash scenario generation and crash causation analysis of AVs.A pre-crash scenario method of roadway-traffic participant-critical event-precrash movement was developed.Thirty one pre-crash scenarios at intersections for AVs were generated using 470 crash reports involving AVs in California.Significant differences between the AV and conventional vehicle(CV)pre-crash scenarios were verified by a statistical analysis.A crash causation method is proposed based on the system control structure,which reveals the interaction relationship between AV crashes and roadway,traffic situation,environment,automated driving system,driver(tester),and vehicle.Nine crash causation patterns and causation chains of AV crashes in the rear-end and lane change scenarios were determined.The results indicate the following:AVs being rear-ended by CVs occurred with a frequency 4.03 times that of rear-ended CVs.The main reasons for rear-end scenarios were that the driver of a CV follows the lead vehicle too closely and insufficient decision-making of the automated driving system to decelerate first,and then stop or start.The main reasons for lane change scenarios were dangerous lane changes or overtaking of CVs,insufficient recognition of other vehicles'lane change intentions by the automated driving system,and unreasonable decision-making of deceleration and collision avoidance.This study promotes the application of scenario-based crash causation analysis methods.It can guide the construction of automated driving test scenarios,and provide a reference for the development and optimization of automated driving systems and improvement in intersection safety.

关 键 词：交通工程 事故致因 事故前场景 交叉口 自动驾驶车辆事故 

分 类 号：U491.3[交通运输工程—交通运输规划与管理]