Path tracking is essential for unmanned driving.This article presents the design of a path tracking system for unmanned trucks,aiming to enhance accuracy and stability across various speeds.The system employs a Linear Quadratic Regulator (LQR) optimized through an improved Genetic Algorithm (GA).First,a two-degree-of-freedom dynamic model and a tracking error model of the vehicle are established based on natural coordinate system.Subsequently,an LQR controller is designed to eliminate steady-state errors and enhance tracking accuracy through feedforward control.Second,the genetic algorithm is enhanced to optimize the weight matrix of the LQR controller,resulting in improved accuracy and stability for path tracking.Finally,the control effectiveness of the designed LQR controller is simulated and verified across a range of operating conditions using the joint simulation platform of Matlab／Simulink and TruckSim.The results show that the GA-optimized LQR (Linear Quadratic Regulator) controller improves the tracking accuracy by about 68.5% and 49.4% at speeds of 30 km/h and 60 km/h,respectively,under the double lane change scenario;while under the U-turn scenario,the tracking accuracy is enhanced by approximately 12.0% and 25.5%,respectively.Specifically,it demonstrates higher stability,with position and heading errors controllable within 0.17 m and 0.11 rad,respectively,thereby validating the efficacy of the proposed tracking control scheme.