Implementing Trajectory Tracking Control Algorithm for Autonomous Vehicle

Duan, X., Wang, Q., Tian, D., Zhou, J., Wang, J., Sheng, Z., Zhao, D. and Sun, Y. (2021) Implementing Trajectory Tracking Control Algorithm for Autonomous Vehicle. In: 4th IEEE International Conference on Unmanned Systems (ICUS 2021), Beijing, China, 15-17 Oct 2021, pp. 947-953. ISBN 9781665438858 (doi: 10.1109/ICUS52573.2021.9641133)

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256996.pdf - Accepted Version



This paper adopts the lateral and longitudinal control decoupling strategy to study the control algorithm of automated driving systems. The Frenet-frame is used for lateral control. According to the lateral error and heading error of the self-driving vehicle and the reference path, the model predictive control (MPC) is used to realize the lateral control of the self-driving vehicle to complete the tracking of the target trajectory. Longitudinal control is separated into two modes: speed control and tracking control. According to the driving environment of autonomous vehicles, a decision-making control strategy is designed to achieve smooth switching of the controller for the two control modes. The speed control mode uses the proportional-integral-derivative (PID) controller to realize the speed tracking control of the reference speed or the driver's setting. The following error model is established for the tracking control mode, and the Linear-Quadratic-Regulator (LQR) controller is used to realize the distance control of the vehicle in front. Finally, the algorithm is validated based on the ROS-CoppeliaSim simulation platform and field testing of autonomous vehicles in the actual tournament. The results show that the lateral and longitudinal decoupling algorithm has a good control effect, environmental adaptability, and stability.

Item Type:Conference Proceedings
Additional Information:This research was supported in part by the National Natural Science Foundation of China under Grant No. 62173012, U20A20155, and 61822101, in part by the Beijing Municipal Natural Science Foundation under Grant No. L191001, in part by the Newton Advanced Fellowship under Grant No. 62061130221, and in part by the Young Elite Scientists Sponsorship Program by Hunan Provincial Department of Education under Grant No. 18B142.
Glasgow Author(s) Enlighten ID:Zhao, Dr Dezong
Authors: Duan, X., Wang, Q., Tian, D., Zhou, J., Wang, J., Sheng, Z., Zhao, D., and Sun, Y.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Published Online:22 December 2021
Copyright Holders:Copyright © 2021 IEEE
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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