Development of a lateral control system for autonomous vehicles by integrating quasi-static and dynamic control methods

Abstract Recent advancements in lateral control systems for autonomous vehicles have focused on achieving high tracking precision and stable control performance. Building on these efforts, this study designs and implements an integrated lateral control system using quasi-static Stanley method and dynamic Proportional, Integral, Derivative (PID) controller. The proposed system works in three stages: positioning, quasi-static control and dynamic control. In the positioning stage, precise positioning is achieved by applying Real-Time Kinematic enhanced positions and headings with velocity obtained from Inertial Measurement Unit. Additionally, a method is proposed to collect and process path coordinate data in environments where predefined path coordinates are unavailable. In the quasi-static control stage, an efficient path tracking control algorithm based on the Stanley method is implemented and applied to the collected ground truth path coordinate data. Finally, in the dynamic control stage, the PID controller is utilized to enhance control precision by minimizing the difference between the vehicle’s actual heading and the target heading response time required to reach the target heading through adjusting the steering wheel angle. The experiment was conducted by driving along two target paths within the GIST campus. On the first target path, Mean Absolute Error (MAE) was recorded at 0.0786 m, and Root Mean Square Error (RMSE) was 0.1140 m. Additionally, the average error between the vehicle’s target heading and actual heading was recorded as 2.1411°. On the second target path, MAE was 0.0427 m, and RMSE was 0.0802 m. The average error between the target heading and actual heading was 2.5000°, demonstrating the performance of the lateral control system. To prove the superiority of the proposed method, thorough collections of prior real-world lateral control systems were exercised, and direct comparisons were made. To the best of our knowledge, the proposed system outperforms previous literature on real-world experiments. We also provide the download link to the target path coordinate data so that future competitions on the best lateral control systems can be held. We believe our work can serve as a foundation for future lateral control systems in the autonomous driving community.