Optimal Path Planning for Autonomous Vehicles Using Artificial Potential Field Algorithm

Abstract

This paper proposes an optimal path planning algorithm to make the autonomous vehicle follow the desired path profile while avoiding nearby obstacles safely. Also, it utilizes only readily available sensors equipped with typical autonomous vehicle system. For optimal path planning, an artificial potential field (APF) algorithm to derive both desired vehicle longitudinal velocity and desired vehicle yaw angle in real time is newly designed, which includes both a repulsive field for avoiding road boundaries and nearby obstacles ahead, and an attractive field for following the proper lane. Next, the path tracking control algorithm consists of longitudinal and lateral motion controllers. Especially, a model predictive control (MPC) for vehicle lateral motion causes the yaw angle error between the desired path profile and the vehicle to approach zero. Then, it can derive an optimal front steering angle considering vehicle state and input constraints. Using CarSim and MATLAB/Simulink simulations, the effectiveness of the proposed algorithm in this paper is verified in some driving scenarios. Accordingly, its high performance for the path planning and tracking of autonomous vehicles can be clearly confirmed.