Post-Impact Motion Planning and Tracking Control for Autonomous Vehicles

There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle collision. Post-impact hazards can be more serious as the driver may fail to maintain effective control after collisions. To avoid subsequent crash events and to stabilize the vehicle, this paper proposes a post-impact motion planning and stability control method for autonomous vehicles. An enabling motion planning method is proposed for post-impact situations by combining the polynomial curve and artificial potential field while considering obstacle avoidance. A hierarchical controller that consists of an upper and a lower controller is then developed to track the planned motion. In the upper controller, a time-varying linear quadratic regulator is presented to calculate the desired generalized forces. In the lower controller, a nonlinear-optimization-based torque allocation algorithm is proposed to optimally coordinate the actuators to realize the desired generalized forces. The proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.