摘要
测试场景构建对于评估自动驾驶汽车(autonomous vehicles,AVs)上路运行风险至关重要。通过收集2019—2021年加州自动驾驶汽车事故报告,首先,将事故序列描述为初始阶段、触发事件、应对动作和碰撞阶段的组合,并从事故报告中提取序列构成基础测试场景。其次,通过序列比对算法衡量序列之间的不相似度。最后,使用聚类分析方法得到9类场景组及其典型场景。频率分析表明最具有代表性的事故模式为AV停止后被后方直行的车辆接触。交叉表分析表明场景组与衡量事故结果和描述道路环境的变量显著相关。研究方法为AV事故场景提取和分类提供新思路,研究结果对了解AV事故模式具有参考价值。
The construction of test scenarios is crucial to evaluate the operational risks of autonomous vehicles(AVs)on the road.By collecting California autonomous vehicle collision report for 2019 to 2021,First,the crash sequence was described as a combination of initial stage,triggering event,response action and crash stage,and the sequences were extracted from crash report to form the basic test scenarios.Second,the dissimilarity between sequences was measured by sequence alignment methods.Finally,nine types of scenario groups and their typical scenarios were obtained using cluster analysis methods.Frequency analysis shows that the most representative crash pattern is when an AV stops and is contacted by a vehicle traveling directly behind it.Cross-tabulation analysis show that scenario groups were significantly associated with variables measuring crash outcomes and describing the road environment.The research methods provide new ideas for AV crash scenarios extraction,and the research results have certain reference value for understanding AV crash pattern.
作者
胡越宁
赵丹
HU Yue-ning;ZHAO Dan(School of Traffic Management,People's Public Security University of China,Beijing 100038,China;Public Security Behavioral Science Laboratory,People's Public Security University of China,Beijing 100038,China)
出处
《科学技术与工程》
北大核心
2023年第11期4908-4916,共9页
Science Technology and Engineering
基金
中国人民公安大学公共安全行为科学与工程科技创新项目(2022KXGCKJ06)
中国人民公安大学中央高校基本科研业务费(2021XKZX06)。
