A hybrid latent class analysis and association rule mining framework to identify injury-related risk patterns in level 2 advanced driver assistance system crashes.

Automated driving features could improve road safety by reducing the number of crashes caused by human error. However, as the market penetration of Level 2 advanced driver assistance systems (ADAS) increases, so does the number of crashes involving these technologies. This study proposes a data-driven framework combining latent class analysis, association rule mining, and logistic regression to analyze 617 Level 2 ADAS crashes reported to the National Highway Traffic Safety Administration between 2019 and 2026. Two latent classes are identified, which are lower-speed local road crashes and high-speed highway crashes. Association rule mining and logistic regression are conducted within each latent class, revealing substantial heterogeneity in injury-related crash patterns. In lower-speed local road crashes, injury occurrences are mainly associated with frontal impacts, intersection-related crashes, and moderate-speed configurations. In high-speed highway crashes, injury occurrences are mainly associated with high-speed configurations, fixed objects, light-to-medium duty vehicles, frontal impacts, and morning or late-night conditions. Practical applications include placing greater emphasis in scenario-based testing for combinations involving moderate and high pre-crash speeds, frontal contact, fixed-object crashes, light-to-medium duty vehicle crash partners, and late-night conditions. These findings may help manufacturers refine speed-aware system-use guidance, driver warnings, and operational design constraints for Level 2 ADAS-equipped vehicles. Overall, this study demonstrates the value of class-specific analysis for uncovering heterogeneous injury-associated patterns in Level 2 ADAS crashes.