Driving with Influence: Exploring Crash Factors of Automated Systems in Different Roadway Contexts

Although SAE Level 2 Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) have been shown to provide some safety benefits, they have largely been constrained to specific driving contexts namely motorways for ADAS and lower speed roadways for ADS. As more advanced systems are entering the roadways and their operating conditions are expanding, it remains an ongoing challenge to assess the safe operation of vehicles with automation in different roadway contexts and leverage lessons learned from real-world incidents to create safer and more robust systems. As of August 2025, the NHTSA’s Standing General Order on Crash Reporting offers systematic data on such incidents, providing at least a cursory overview of where and how they occur. From this source, a total of 2,263 crash records were extracted—472 for ADAS systems and 1,791 for ADS systems. Through the application of association rule mining and a novel metric termed influence, patterns in ADAS- and ADS-related crashes were examined within different roadway contexts. In intersections, we observed that approximately 29% of frequent itemsets were common to both system types, with 33% appearing uniquely in ADAS crashes and 39% uniquely in ADS crashes. On highways, the overlap was somewhat higher at 36%, while unique itemsets accounted for roughly 28% of ADAS crashes and 36% of ADS crashes. The results offer an initial, exploratory perspective on the impact of vehicle automation on public roadways, providing insights that can inform system-specific safety assessments, risk mitigation strategies, and future research into the evolving dynamics of automated driving technologies.