Crash Severity in Automated Vehicle Collisions: A Texas-Based Investigation Across Automation Levels

Abstract

Abstract Crash severity remains a critical issue despite rapid advances in vehicle automation. This study contributes to automated vehicle (AV) safety research by analyzing a Texas crash dataset that spans multiple automation levels, unlike most studies that focus on California. Using Association Rules Mining (ARM), it identifies key relationships among crash factors, such as speed, road type, collision type, lighting, weather, and driver characteristics. Crashes were grouped by automation level: assisted driving (Level 1), partial automation (Level 2), and conditional–full automation (Levels 3–5). In assisted driving, severe injuries were frequently linked to high speeds on narrow roads and older drivers. For partial automation, moderate speeds combined with poor traffic control or complex layouts heightened injury risk. Crashes involving Levels 3–5 were not observed in the KA category, suggesting a lower observed severity profile for higher-automation crashes in this dataset. An interactive tool was also developed to visualize crash locations and factors by severity and automation level. Policy recommendations include improving roadway design in high-risk areas, enhancing driver training, and advancing AV system safety. These strategies can reduce crash severity and support safer AV deployment in Texas and beyond.