Pan Yiyong, Yang Saisai, Wang Congwei
College of Automotive and Transportation Engineering, Nanjing Forestry University, Nanjing, China.
Traffic Inj Prev. 2025 Aug 15:1-8. doi: 10.1080/15389588.2025.2492821.
Understanding the factors influencing crash severity of autonomous vehicles is important for increasing road safety. This study focuses on a multi-source accident dataset of vehicles equipped with autonomous driving systems to explore the endogenous relationship between manual takeover of autonomous vehicles and the severity of crash, as well as the influencing factors.
By screening and summarizing data on autonomous vehicle accidents. We choose self-driving car takeover and crash severity as potential variables to build a structural equation model to explore the influences of crash severity through continuous variable updating and path improvement. We select autonomous vehicle takeover and crash severity as potential variables and designed a structural equation model to explore the factors affecting crash severity through continuous variable updating and path improvement. Meanwhile, we establish a generalized linear logit model to analyze the factors affecting manual takeover. Finally, the intrinsic link between crash severity and manual takeover is discussed through path analysis and comparison of model results.
Cloudy and rainy weather, left rear of vehicle contact area, and daylight lighting significantly impact manual takeover and crash severity. Specifically, wet road surface, rainy weather, and daylight have relatively more significant effects on takeover in the structural equation model. And takeover, roadway type including non-freeway and intersection can significantly impact crash severity. Additionally, the study demonstrates the endogeneity between crash severity and takeover at the time of autonomous vehicle crash.
This study analyzes the potential relationships and influencing factors between takeover events of autonomous vehicles and crash severity. It is found that the frequency of takeover events significantly increases when driving in rainy weather and at night. It is suggested that a real-time monitoring module for adverse weather or lighting conditions should be added to the autonomous driving system to provide early warnings and reduce the occurrence of takeover events, thereby enhancing the safety and reliability of autonomous vehicles.
了解影响自动驾驶车辆碰撞严重程度的因素对于提高道路安全至关重要。本研究聚焦于配备自动驾驶系统车辆的多源事故数据集，以探究自动驾驶车辆人工接管与碰撞严重程度之间的内生关系及其影响因素。
通过筛选和汇总自动驾驶车辆事故数据。我们选择自动驾驶汽车接管和碰撞严重程度作为潜在变量，构建结构方程模型，通过连续变量更新和路径改进来探究碰撞严重程度的影响因素。我们选择自动驾驶车辆接管和碰撞严重程度作为潜在变量，并设计了一个结构方程模型，通过连续变量更新和路径改进来探究影响碰撞严重程度的因素。同时，我们建立广义线性logit模型来分析影响人工接管的因素。最后，通过路径分析和模型结果比较来探讨碰撞严重程度与人工接管之间的内在联系。
多云和雨天天气、车辆左后部接触区域以及日光照明显著影响人工接管和碰撞严重程度。具体而言，在结构方程模型中，湿滑路面、雨天天气和日光对接管的影响相对更为显著。并且，接管、包括非高速公路和十字路口在内的道路类型会显著影响碰撞严重程度。此外，该研究证明了自动驾驶车辆碰撞时碰撞严重程度与接管之间的内生性。
本研究分析了自动驾驶车辆接管事件与碰撞严重程度之间的潜在关系及其影响因素。研究发现，在雨天和夜间驾驶时，接管事件的频率显著增加。建议在自动驾驶系统中添加针对恶劣天气或光照条件的实时监测模块，以提供早期预警并减少接管事件的发生，从而提高自动驾驶车辆的安全性和可靠性。
