Wang Jie, Chen Yibo, Li Shun, Gao Zhibo, Xiang Jian
School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha, Hunan, China.
Traffic Inj Prev. 2025;26(5):540-547. doi: 10.1080/15389588.2024.2439962. Epub 2025 Jan 13.
This study aims to investigate the causes of 2-vehicle collisions involving an autonomous vehicle (AV) and a conventional vehicle (CV). Prior research has primarily focused on the causes of crashes from the perspective of AVs, often neglecting the interactions with CVs.
To address this limitation, the study proposes a classification framework for crash causation patterns in 2-vehicle collisions involving an AV and a CV, considering their interactions. The framework categorizes the crash causation patterns into 5 distinct types: (1) failure of the AV system, (2) failure of takeover control, (3) driver error after takeover, (4) CV failure to adapt to unforeseen changes in AV behaviors, and (5) other factors related to the CV. Utilizing the AV crash data set proposed by Zheng et al., this study extracted 450 two-vehicle collisions involving AVs and CVs for our analysis.
Our analysis reveals that the majority of 2-vehicle collisions are triggered by CVs, specifically identified in patterns 4 and 5. Pattern 4 is the primary crash causation factor, accounting for 55% of total collisions. The leading contributing factor to pattern 4 is the improper response of CVs to AVs stopping. There are notable variations in crash injury severity, collision type, and environmental conditions across different causation patterns. Crashes stemming from human drivers' errors (patterns 3, 4, and 5) are more likely to result in moderate to severe injuries. Specifically, pattern 4 notably exhibits the highest likelihood of causing rear-end collisions, whereas patterns 1 and 5 are more prone to causing side collisions. Additionally, crashes associated with pattern 4 are more frequently observed in locations with traffic controls or obstacles.
Based on our findings, we propose several recommendations for manufacturers to enhance AV safety performance. These include minimizing planning errors in autonomous driving algorithms, improving communication abilities between AVs and other road users for smoother interactions and better anticipation of actions, and providing specialized driver training for navigating mixed environments with both AVs and CVs.
本研究旨在调查涉及自动驾驶汽车（AV）和传统汽车（CV）的两车碰撞事故原因。先前的研究主要从自动驾驶汽车的角度关注碰撞原因，常常忽视与传统汽车的相互作用。
为解决这一局限性，本研究提出了一个涉及自动驾驶汽车和传统汽车的两车碰撞事故因果模式分类框架，考虑到它们之间的相互作用。该框架将碰撞因果模式分为5种不同类型：（1）自动驾驶系统故障；（2）接管控制失败；（3）接管后驾驶员失误；（4）传统汽车未能适应自动驾驶汽车行为的意外变化；（5）与传统汽车相关的其他因素。利用郑等人提出的自动驾驶碰撞数据集，本研究提取了450起涉及自动驾驶汽车和传统汽车的两车碰撞事故进行分析。
我们的分析表明，大多数两车碰撞事故是由传统汽车引发的，具体体现在模式4和模式5中。模式4是主要的碰撞因果因素，占总碰撞事故的55%。模式4的主要促成因素是传统汽车对自动驾驶汽车停车的不当反应。不同因果模式下的碰撞伤害严重程度、碰撞类型和环境条件存在显著差异。由人类驾驶员失误导致的碰撞事故（模式3、4和5）更有可能造成中度至重度伤害。具体而言，模式4显著表现出导致追尾碰撞的最高可能性，而模式1和模式5更容易导致侧面碰撞。此外，与模式4相关的碰撞事故在有交通管制或障碍物的地点更频繁出现。
基于我们的研究结果，我们为制造商提出了几项提高自动驾驶汽车安全性能的建议。这些建议包括尽量减少自动驾驶算法中的规划错误，提高自动驾驶汽车与其他道路使用者之间的通信能力，以实现更顺畅的交互和更好的行动预判，并为在自动驾驶汽车和传统汽车混合的环境中驾驶提供专门的驾驶员培训。
