Advancing crash investigation with connected and automated vehicle data

Given that the Connected and Automated Vehicle (CAV) era is rapidly unfolding, this study seeks to leverage newly available CAV data to improve crash investigation procedures and obtain input from stakeholders, specifically law enforcement. In particular, law enforcement use of existing Event Data Recorders (EDRs), which store vehicle kinematics during a crash, is explored. Crash investigations are currently aided by EDRs, but this aid could be expanded to include the information gathered by Automated Driving System (ADS) technologies such as radar, cameras, LiDAR, infrared, and ultrasonic. This detailed data could improve the fidelity of future crash investigations, with potential new information such as driver/operator state, vehicle automation capabilities, location, objects and people in the immediate area, performance and diagnostic data, and environmental factors. Through text mining analysis of CAV and sensor-related literature and interviews with law enforcement, this study contributes by gathering evidence about crash investigations to pinpoint the contributing factors of a crash. Further we explore law enforcement involvement in the design of the current EDR retrieval process and their knowledge about using ADS data. Broadly, the project applies the safe systems approach by suggesting a framework that integrates CAV data in the new crash investigation procedures.