Research on intelligent vehicle path tracking control based on preview distance optimization

Abstract

This paper proposes an intelligent vehicle lateral-vertical cooperative control method based on optimized preview distance to address the coupling and conflict issues between the path tracking system and the handling stability in the process of controlling the motion of intelligent vehicles. To begin with, an intelligent vehicle path tracking control system based on expected yaw velocity has been designed by establishing a three-degree-of-freedom dynamic model for intelligent vehicles. Then, we analyzed the mechanism by which changes in vehicle speed, road curvature, and preview distance affect the accuracy of vehicle path tracking and handling stability. Considering the "human-vehicle-road" system in intelligent transportation systems, critical values for collision and instability were set. Furthermore, we designed a proactive optimization method for preview distance under different working conditions, using an optimization algorithm to improve path tracking accuracy while ensuring vehicle stability, based on the lateral displacement deviation and lateral lateral orientation deviation representing the accuracy of path tracking, as well as the lateral acceleration representing handling stability. Finally, HIL platform test was conducted. The simulation and test results show that the optimized path tracking algorithm reduces lateral deviation to as low as 0.05 meters, and the stability constraint control in the algorithm can be triggered promptly even under extreme conditions. The research findings provide a theoretical reference for the lateral and vertical coordinated control of intelligent vehicles in the future.