In order to improve the accuracy of structured road boundary detection and solve the problem of the poor robustness of single feature boundary extraction, this paper proposes a multi-feature road boundary detection algorithm based on HDL-32E LIDAR. According to the road environment and sensor information, the former scenic cloud data is extracted, and the primary and secondary search windows are set according to the road geometric features and the point cloud spatial distribution features. In the search process, we propose the concept of the largest and smallest cluster points set and a two-way search method. Finally, the quadratic curve model is used to fit the road boundary. In the actual road test in the campus road, the accuracy of the linear boundary detection is 97.54%, the accuracy of the curve boundary detection is 92.56%, and the average detection period is 41.8 ms. In addition, the algorithm is still robust in a typical complex road environment.

Aiming at the problems of model uncertainty, external interference of road conditions, and inherent nonlinearity of lateral movement of autonomous driving vehicles, this paper proposes an effective control technology for an automatic steering system, namely, the receding horizon $H_{\infty }$ control, which has good path tracking performance and can overcome external interference. First, a two-degree-of-freedom vehicle model and a road model are established. Then, the Hamilton equation is established using Pontriagin’s minimum principle, and the optimal control solution and the most serious disturbance are solved based on the minimax principle. Finally, in order to evaluate the receding horizon $H_{\infty }$ autonomous steering control method, lane change simulation and hardware in the loop simulation are developed to describe the effect of receding horizon $H_{\mathrm {\infty }}$ . The simulation results show that the model predictive control (MPC) and linear matrix inequality (LMI) controller will exhibit oscillation and instability under the condition of external interference. To improve the stability, the $H_{\infty }$ controller reduces part of the performance, while the rolling horizon $H_{\mathrm {\infty }}$ strategy can ensure the robustness of automatic steering and has a certain anti-interference performance.