Causation analysis of crashes and near crashes using naturalistic driving data
Causation analysis of crashes and near crashes using naturalistic driving data
复制标题
使用自然驾驶数据对碰撞和接近碰撞的原因进行分析
DOI：
10.1016/j.aap.2022.106821
复制
发表时间：
2022-08-30
影响因子：
5.9
通讯作者：
Chen，Xiaohong
中科院分区：
文献类型：
--
作者：
Wang，Xuesong;Liu，Qian;Chen，Xiaohong
在交通安全领域中，理解事故原因以采取对策一直是一个焦点，也是一个难点。以往的研究一直受到碰撞数据不足和更适合于单一碰撞的分析方法的限制。使用碰撞和接近碰撞（CNCs）和自然驾驶研究可以帮助解决数据问题，使用碰撞前场景可以确定给定场景中多次碰撞的高流行率原因。因此，本研究提出了一个两阶段的碰撞因果关系分析方法的基础上，预碰撞场景和碰撞因果关系推导框架，系统地分类和分析的影响因素。从上海自然驾驶研究（SH-NDS）中提取了536个CNCs，并根据美国国家公路交通安全管理局（NHTSA）的预碰撞情景类型学将其分为23个不同的预碰撞情景。进行了深入的调查，并使用所提出的框架，确定因果关系模式的基础上，框架的道路使用者，车辆，道路基础设施和道路环境子系统的相互作用的共享相同的情况下进行了分析。通过统计分析，因果关系的模式和他们的影响因素进行了比较，为三个常见的前碰撞的情况下，最高的发病率：追尾，车道变化，和车辆-pedalcyclist。低速跟车时的刹车失误、跟车过近以及非驾驶相关的注意力分散是造成追尾的重要原因。在变道场景中，主要的致因模式包括违规使用转向灯和危险变道作为关键因素。骑自行车的场景尤其受到视觉障碍、非机动车车道不足和骑自行车的人违反交通规则的影响。基于识别的因果模式及其影响因素，针对三种常见情况提出了对策，为安全改进项目和先进驾驶辅助系统的开发提供支持。
Understanding crash causation to the extent needed for applying countermeasures has always been a focus as well as a difficulty in the field of traffic safety. Previous research has been limited by insufficient crash data and analysis methods more suitable to single crashes. The use of crashes and near crashes (CNCs) and naturalistic driving studies can help solve the data problem, and use of pre-crash scenarios can identify the high-prevalence causes across multiple crashes of a given scenario. This study therefore proposes a two-stage crash causation analysis method based on pre-crash scenarios and a crash causation derivation framework that systematically categorizes and analyzes contributing factors. From the Shanghai Naturalistic Driving Study (SH-NDS), 536 CNCs were extracted, and were grouped into 23 different pre-crash scenarios based on the National Highway Traffic Safety Administration (NHTSA) pre-crash scenario typology. In-depth investigations were conducted, and CNCs sharing the same scenario were analyzed using the proposed framework, which identifies causation patterns based on the interaction of the framework’s road user, vehicle, roadway infrastructure, and roadway environment subsystems. Through statistical analysis, the causation patterns and their contributing factors were compared for three common pre-crash scenarios of highest incidence: rear-end, lane change, and vehicle-pedalcyclist. Braking error in low-speed car following, following too closely, and non-driving-related distraction were important causes of rear-end scenarios. In lane change scenarios, the main causation patterns included illegal use of turn signals and dangerous lane changes as critical factors. Pedalcyclist scenarios were particularly impacted by visual obstructions, inadequate lanes for non-motorized vehicles, and pedalcyclists violating traffic regulations. Based on the identified causation patterns and their contributing factors, countermeasures for the three common scenarios are suggested, which provide support for safety improvement projects and the development of advanced driver assistance systems.