Abstract: Based on the road information, using the driver preview model to generate the actuator input is one of the main method in unmanned vehicle path tracking, but for higher speed and small turn radius, the error of the driver preview model may induce less driving comfort and the vehicle even lose stability. In order to improve the accuracy of path tracking and ensure its steering frequency and stability, an adaptive optimization control strategy for preview distance is proposed, which is based on particle swarm optimization(PSO) algorithm. Firstly, based on the tracking error, designed a driver preview model by using slide mode control. Secondly, an adaptive algorithm based on PSO is proposed, which takes into account of tracking accuracy, steering frequency and stability. Finally, the proposed algorithm is tested on co-simulation platform of CarSim and Simulink software and driving simulator test bench. The results show that the algorithm is effective and it can ensure path tracking accuracy, steering controllability and stability. The optimized preview distance driver model combined the information of road and speed, and provides reliable input for unmanned vehicle path tracking control.