Research on a Path Tracking Control Strategy for Autonomous Vehicles Based on State Parameter Identification

Abstract

With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for achieving more efficient control. First, a three-degrees-of-freedom vehicle dynamics model based on the Dugoff tire model is constructed to accurately estimate key vehicle state parameters. Next, UKF is used to estimate road friction coefficients and key vehicle state parameters, and its performance is compared with Extended Kalman Filtering (EKF) under various conditions. The results show the superiority of UKF in identifying road friction coefficients. Based on SMC theory, a sliding surface is designed, and the functional relationship between state variables and control variables is derived to establish the corresponding control model. Joint simulations using Carsim and Simulink under different conditions validate the real-time performance and effectiveness of the designed UKF-SMC integrated control strategy in the presence of external disturbances and system uncertainties. Simulation results indicate that this strategy effectively enhances the overall performance and safety of autonomous vehicles, providing an accurate real-time solution capable of handling complex and variable road conditions. The proposed UKF-SMC integrated control strategy not only proves its theoretical superiority but also demonstrates promising practical applications in simulation experiments. This study provides reliable technical support for the development of autonomous driving technology under complex road conditions.

Keywords

• autonomous driving
• unscented Kalman filtering
• sliding mode control
• path tracking
• vehicle dynamics model