School of Transportation, Southeast University, Nanjing 211189, China.
Institute of Smart City and Intelligent Transportation, Institute of Urban Rail Transportation, Southwest Jiaotong University, Chengdu 611730, China.
Accid Anal Prev. 2024 May;199:107492. doi: 10.1016/j.aap.2024.107492. Epub 2024 Feb 29.
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.
本研究旨在探讨造成自动驾驶汽车（AV）事故的风险因素及其相互关系。从加利福尼亚州机动车辆管理局（DMV）发布的自动驾驶汽车碰撞报告中收集了 2015 年至 2023 年期间的自动驾驶汽车事故数据。根据车辆损坏情况，将自动驾驶汽车事故分为四类。自动驾驶汽车事故特征包括事故地点和时间、驾驶模式、车辆运动、事故类型和车辆损坏、交通状况等，这些特征被用作潜在的风险因素。采用关联规则挖掘方法（ARM）来识别经常同时发生在自动驾驶汽车事故中的一组组风险因素。一些关联规则表明，自动驾驶汽车事故是由道路因素、车辆因素和环境条件之间的复杂相互作用造成的。无损坏和轻微事故更可能受到道路特征和交通状况的影响。相比之下，车辆的运动对严重自动驾驶汽车事故更为敏感。不当的车辆操作会增加严重自动驾驶汽车事故的概率。此外，结果表明恶劣天气条件可能会增加自动驾驶汽车事故的损坏程度。自动驾驶汽车与道路边缘基础设施或夜间湿路面上的弱势道路使用者的相互作用可能会导致生命和财产的重大损失。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，自动驾驶模式可以降低碰撞损坏的风险，而在常规驾驶模式下则不然。本研究的结果应该为提高道路上自动驾驶汽车的安全性和效率的政策措施和工程对策提供参考，最终提高道路运输的整体安全性和可靠性。
