Behavioral Competence Tests for Highly Automated Vehicles

It is necessary to evaluate the safety of highly automated vehicles (HAVs) rigorously before their deployment on public roads. This paper describes a procedure to conduct behavioral competence tests for HAVs using the unprotected left-turn as the illustrating scenario. We first describe a model-based method for test case generation. Subsequently, we propose two methods for synchronizing the motions of the primary other vehicle (POV) and the vehicle under test (VUT), one using fixed speed profile, the other using model predictive control (MPC). Finally, we implement the POV algorithm for the left-turn scenario on an experimental vehicle, and conduct field tests using both virtual and real VUT in the Mcity test facility.