Normalizing Crash Risk of Partially Automated Vehicles under Sparse Data

Abstract

The safety of increasingly automated vehicles is of great concern to regulators, yet crash rates are generally reported by manufacturers with proprietary metrics. Without consistent definitions of crashes and exposure, comparing automated vehicle crash rates with baseline datasets becomes challenging. This study investigates the reported on-road crash rates of one manufacturer’s partially automated driving system. Their reported crash rates are adjusted based on roadway classification and driver demographics to allow for direct comparison with the manufacturer’s own advanced driver assistance systems. Recommendations for uniform crash reporting standards are provided.