The Implementation of Safety Acceptance Evaluation for Highly Automated Vehicles

Abstract

Safety evaluation is crucial for the mass deployment of highly automated vehicles (HAVs). This paper presents a procedure for conducting behavior competence testing for HAVs. First, we describe an efficient test case generation scheme for cut-in and unprotected left turn scenario. We then design the algorithm for synchronizing the primary other vehicle with the vehicle under test based on optimal control. Then, scoring criteria for quantifying HAV performances is presented. Finally, we implement the testing procedure in both simulations and real world. Accurate and repeatable testing is achieved, and quantitative evaluation results are acquired for a baseline HAV.