Path planning and tracking system based on MPC-APF-EKF for autonomous vehicle local obstacle avoidance

Abstract This paper introduces a control system intended for autonomous vehicles, which consists mainly of a trajectory re-planning module capable of obstacle avoidance and a tracking control module. In the trajectory re-planning module, the Model Predictive Control (MPC) and Artificial Potential Field (APF) method are applied in combination. Generated by the APF, the attractive and repulsive fields improve the effectiveness of obstacle avoidance in conjunction with the MPC. Also, a new obstacle avoidance function is developed within this module. In the tracking control module, an approach that integrates MPC with Extended Kalman Filter (EKF) is adopted. Due to the high performance of the EKF in state estimation, the control accuracy of the MPC is enhanced, which enables precise tracking of the planned path. The proposed control system is validated by conducting joint simulations via Matlab-Simulink and Carsim. According to the experimental results, the proposed system demonstrates effective performance in both obstacle avoidance and path tracking.