Optimal Driving Control for Autonomous Electric Vehicles Based on In-Wheel Motors Using an Artificial Potential Field

• Published in: IEEE access Vol. 12; p. 1
• Main Authors: Park, Giseo, Kim, Sooyoung, Kang, Hyeongmuk
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; artificial potential field; autonomous electric vehicle; Autonomous vehicles; Barriers; Control algorithms; Control theory; Electric potential; Electric vehicles; in-wheel motor; Optimal control; Optimal driving control; Optimization; Path planning; Path tracking; Potential fields; Predictive control; Roads; Steering; Torque; Tracking; Wheels; Yaw
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3443869

In this paper, we propose an optimal driving control algorithm that enables autonomous electric vehicle to track the reference path while avoiding front obstacles safely and quickly. An artificial potential field (APF) consists of a repulsive field that prevents collisions with nearby obstacles and road boundaries and an attractive field that brings the autonomous electric vehicle closer to the reference path. This APF is used as a path planning technique in this paper. Both the target longitudinal velocity and target yaw angle from the APF are transmitted to the model predictive controller (MPC) for path tracking of the autonomous electric vehicle. It predicts the future vehicle lateral behavior after a few seconds and optimally outputs the fast and accurate front steering angle and each wheel longitudinal force. In particular, the target vehicle longitudinal force for following the target longitudinal speed is set as the control input constraint of the proposed MPC. Based on CarSim and MATLAB/Simulink co-simulations, the high performance of the optimal driving control algorithm proposed in this paper is clearly confirmed in some driving scenario tests.