Crash mitigation controller for unavoidable T-bone collisions using reinforcement learning

Abstract

T-bone collision constitutes an emergency crash scenario that results in casualties and heavy losses; it is an excessively complicated scenario that cannot be handled by conventional control systems. This paper presents an innovative crash mitigation controller for application during unavoidable T-bone collisions to expand the vehicle-maneuverability envelope and minimize crash severity; this controller combines prior knowledge using an optimum expert-behavior policy and drift-operation mechanism based on an improved reinforcement learning algorithm, TD3. Vehicle and tire modeling are performed considering the nonlinear and coupled dynamics characteristics to improve control accuracy. Unlike conventional control systems and other reinforcement learning algorithms, the proposed controller realizes the optimum crash mitigation effect under different scenarios. It is expected to afford autonomous driving technologies with enhanced operating capabilities under extreme conditions.