Pattern recognition in crash clusters involving vehicles with advanced driving technologies

Abstract

Autonomous Vehicle (AV) technologies, including Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), have significant potential to reduce crashes caused by driver errors. However, as AVs become more prevalent on roadways, the number of crashes involving them is also increasing. While considerable research has explored factors contributing to AV crashes, a gap remains in understanding the critical risk factor patterns within clusters of ADAS- and ADS-engaged AV crashes. To address this gap, this study employs the Cluster Correspondence Analysis tool to cluster crash-related factors. The analysis identified three distinct clusters for both ADAS- and ADS-engaged AV crashes. For ADAS-engaged AVs, the most representative cluster involves fatal crashes at intersections, particularly those involving left-turning vehicles. In contrast, ADS-engaged AV crashes most commonly occur in daylight and involve non-motorists. Key differences were observed: when ADAS is engaged, rear-end crashes typically result in property damage only, whereas ADS-engaged rear-end crashes are more likely to cause minor injuries. However, a notable similarity is that high-speed roads (with posted speed limits of 71 mph or higher) frequently feature animals as crash partners in both ADAS- and ADS-engaged crashes. Based on these findings, it is strongly recommended to focus on infrastructural improvements alongside enhancing AV algorithms and sensor performance, particularly for non-motorist and animal detection in low-light conditions. Policymakers should prioritize driver education on safe AV operation and interaction while also mandating the installation of external human–machine interfaces to enhance AV communication with other road users and reduce rear-end crashes.