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Automated vehicle crash sequences: Patterns and potential uses in safety testing
Accident Analysis & Prevention ( IF 6.376 ) Pub Date : 2021-02-09 , DOI: 10.1016/j.aap.2021.106017
Yu Song , Madhav V. Chitturi , David A. Noyce

With safety being one of the primary motivations for developing automated vehicles (AVs), extensive field and simulation tests are being carried out to ensure AVs can operate safely on roadways. Since 2014, the California Department of Motor Vehicles (DMV) has been collecting AV collision and disengagement reports, which are valuable data sources for studying AV crash patterns. A crash sequence of events describes the AV’s interactions with other road users before a collision in a temporal manner. In this study, sequence of events data extracted from California AV collision reports were used to investigate patterns and how they may be used to develop AV test scenarios. Employing sequence analysis methods and clustering, this study evaluated 168 AV crashes (with AV in automatic driving mode before disengagement or collision) reported to the California DMV from 2015 to 2019. Analysis of subsequences showed that the most representative pattern in AV crashes was “collision following AV stop”. Analysis of event transition showed that disengagement, as an event in 24% of all studied AV crash sequences, had a transition probability of 68% to an immediate collision. Cluster analysis characterized AV crash sequences into seven groups with distinctive crash dynamic features. Cross-tabulation analysis showed that sequence groups were significantly associated with variables measuring crash outcomes and describing environmental conditions. Crash sequences are useful for developing AV test scenarios. Based on the findings, a scenario-based AV safety testing framework was proposed with sequence of events embedded as a core component.



中文翻译:

自动车辆碰撞序列:安全测试的模式和潜在用途

安全是开发自动驾驶汽车(AV)的主要动机之一,因此正在进行广泛的现场和模拟测试,以确保自动驾驶汽车可以在道路上安全运行。自2014年以来,加利福尼亚机动车局(DMV)一直在收集AV碰撞和脱离接触报告,这是研究AV碰撞模式的宝贵数据来源。事件的碰撞顺序描述了在碰撞之前,AV与其他道路用户的交互。在这项研究中,从加利福尼亚州AV碰撞报告中提取的事件数据序列用于调查模式以及如何将其用于开发AV测试方案。利用序列分析方法和聚类,这项研究评估了2015年至2019年间报告给加利福尼亚DMV的168起AV碰撞(在脱离或碰撞之前处于自动驾驶模式)。对子序列的分析表明,AV碰撞中最具代表性的模式是“ AV停止后发生碰撞”。对事件过渡的分析表明,在所有研究的AV碰撞序列中有24%的事件是脱离接触,因此有68%的过渡概率立即发生碰撞。聚类分析将AV碰撞序列分为7组,具有独特的碰撞动态特征。交叉表分析表明,序列组与测量碰撞结果和描述环境条件的变量显着相关。崩溃序列对于开发AV测试方案很有用。根据调查结果,

更新日期:2021-02-09
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