Research on Trajectory Tracking of Autonomous Vehicle Based on Lateral and Longitudinal Cooperative Control

Abstract

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicles require the collaborative operation of multiple modules during their journey, and enhancing tracking performance is a key focus in the field of planning and control. To address this challenge, we propose a cooperative control strategy, which is designed based on the integration of model predictive control (MPC) and a dual proportional–integral–derivative approach, referred to as collaborative control of MPC and double PID (CMDP for short in this article).The CMDP controller accomplishes the execution of actions based on information from perception and planning modules. For lateral control, the MPC algorithm is employed, transforming the MPC’s optimal problem into a standard quadratic programming problem. Simultaneously, a fuzzy control is designed to achieve adaptive changes in the constraint values for steering angles. In longitudinal control, a dual control strategy comprising position-type PID and velocity-type PID is used, decoupling lateral and longitudinal calculations. The collaborative control strategy links lateral and longitudinal aspects, aiming to reduce computational complexity while enhancing control effectiveness. For local path planning, a fifth-degree polynomial is employed for path optimization to improve stability in responding to controller commands. Simulation experiments conducted on the CARLA-ROS joint simulation platform in realistic scenarios show that the model exhibits high accuracy and minimal tracking error under dual lane-changing conditions. Comparative experiments demonstrate superior control performance of the proposed model over traditional MPC controllers.</div></div>