Analysis of factors contributing to severity of AV crashes at intersections: Insights from the Autonomous Vehicle Operation Incident Dataset Across The Globe (AVOID).

Abstract

Objectives: The market for autonomous vehicles (AVs) is developing rapidly, while safety concerns persist as a critical challenge hindering their widespread development and commercialization. Intersections, characterized by highly dynamic and unpredictable traffic conditions, represent particularly high-risk scenarios for AVs. This study systematically investigates the key risk factors influencing collision severity at intersections to enhance AV safety performance.

Methods: This study employs the Autonomous Vehicle Operation Crash Dataset Across The Globe (AVOID) to investigate the key factors influencing the injury severity of AV related collisions. A hybrid analytical framework is proposed, integrating an XGBoost-SHAP model for feature importance analysis and a multinomial logit (MNL) model for statistical inference. Following the factor importance ranking, nine critical determinants were selected to examine their individual effects on injury severity levels. Furthermore, the XGBoost-SHAP approach was utilized to explore interaction effects among significant factors, revealing synergistic relationships between key features.

Results: The results indicate that the majority of crashes at intersections occurred when AVs were stationary or moving at low speeds (17.71% while stopped, 42.76% at speeds below 10 mph). Approximately 58.14% of the crashes involved autonomous driving mode, with an injury rate 14.5% higher compared to manual mode. Factors such as pre-crash movement, crash scene, contact area, pre-crash speed, and autonomous mode significantly influenced injury severity. Crashes occurring during straight-line travel or lane changes in autonomous mode tended to result in more severe injury compared to manual driving. Additionally, crashes in steering direction scenes at speeds between 10 and 20 mph were associated with higher injury severity, and speeds exceeding 20 mph in traffic through scenes led to even more severe injuries.

Conclusions: This study reveals the main factors influencing the severity of collisions in autonomous vehicles and the combination of the following factors that may increase the severity of injuries: autonomous driving mode, lane changing, turning or crossing scenarios, and high-speed driving. The results advance the understanding of autonomous vehicle safety and offer potential implications for enhancing self-driving systems.