Potential Field Based Path Planning with Predictive Tracking Control for Autonomous Vehicles

Abstract

Path planning and tracking of autonomous vehicles are generally two independent tasks accomplished through traffic environment modeling, reference trajectory generation and vehicle motion control. In this paper, we proposed a unified path planning and tracking method utilizing the optimization algorithm of the model predictive control to generate optimal reference trajectory and vehicle motion control concurrently. The vehicle’s surroundings including obstacle vehicles and road marks are firstly reconstructed based on the artificial potential field approach which generates a reference trajectory, then the total potential of the traffic environment is incorporated into the cost function of the model predictive controller. Therefore, the path planning and tracking of the vehicle can be unified for collision avoidance with moving or fixed obstacles in its surrounding traffic environment. The simulation shows that this unified path planning and tracking method is capable of accomplishing the obstacle avoidance for the vehicle during severe traffic scenarios.