The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by California Department of Motor Vehicles (DMV). AV crashes were categorized into four types based on vehicle damage. AV crashes features including crash location and time, driving mode, vehicle movements, crash type and vehicle damage, traffic conditions, and among others were used as potential risk factors. Association Rule Mining methods (ARM) were utilized to identify sets of contributing risky factors that often occur together in AV crashes. Several association rules suggest that AV crashes result from complex interactions between road factors, vehicle factors, and environmental conditions. No damage and minor crashes are more likely affected by the road features and traffic conditions. In contrast, the movements of vehicles are more sensitive to severe AV crashes. Improper vehicle operations could increase the probability of severe AV crashes. In addition, results suggest that adverse weather conditions could increase the damage of AV crashes. AV interactions with roadside infrastructure or vulnerable road users on wet road surfaces during the night could potentially lead to significant loss of life and property. Furthermore, the safety effects of vehicle mode on the different AV crash damage are revealed. In some contexts, the autonomous driving mode can mitigate the risk of crash damages compared with conventional driving mode. The findings of this study should be indicative of policy measures and engineering countermeasures that improve the safety and efficiency of AV on the road, ultimately improving road transportation's overall safety and reliability.

中文翻译：

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我们可以从 AV 事故中学到什么？– 用于识别影响风险因素的关联规则分析

本研究的目的是探讨导致自动驾驶汽车 (AV) 碰撞的危险因素及其相互依赖性。2015 年至 2023 年自动驾驶汽车碰撞数据收集自加州机动车辆管理局 (DMV) 发布的自动驾驶汽车碰撞报告。根据车辆损坏程度，自动驾驶汽车碰撞事故可分为四种类型。AV碰撞特征包括碰撞地点和时间、驾驶模式、车辆运动、碰撞类型和车辆损坏、交通状况等被用作潜在的风险因素。关联规则挖掘方法 (ARM) 用于识别在 AV 事故中经常同时发生的一组危险因素。一些关联规则表明，自动驾驶汽车碰撞事故是由道路因素、车辆因素和环境条件之间复杂的相互作用造成的。无损坏和轻微碰撞更有可能受到道路特征和交通状况的影响。相比之下，车辆的运动对严重的自动驾驶碰撞事故更为敏感。车辆操作不当可能会增加严重自动驾驶汽车事故的可能性。此外，研究结果表明，恶劣的天气条件可能会增加自动驾驶汽车事故的损失。夜间，自动驾驶汽车与路边基础设施或潮湿路面上的弱势道路使用者的互动可能会导致重大的生命和财产损失。此外，还揭示了车辆模式对不同自动驾驶汽车碰撞损坏的安全影响。在某些情况下，与传统驾驶模式相比，自动驾驶模式可以降低碰撞损坏的风险。这项研究的结果应该为提高自动驾驶汽车在道路上的安全性和效率提供政策措施和工程对策，最终提高道路交通的整体安全性和可靠性。