What can we learn from the AV crashes? – An association rule analysis for identifying the contributing risky factors

毒物控制 人为因素与人体工程学 职业安全与健康 伤害预防 联想(心理学) 自杀预防 工程类 运输工程 法律工程学 环境卫生 计算机安全 心理学 计算机科学 应用心理学 医学 病理 心理治疗师
作者
Pei Liu,Yanyong Guo,Pan Liu,Hongliang Ding,Jiandong Cao,Jibiao Zhou,Zhongxiang Feng
出处
期刊:Accident Analysis & Prevention [Elsevier BV]
卷期号:199: 107492-107492
标识
DOI:10.1016/j.aap.2024.107492
摘要

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.
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