Development of a basic GNSS-based lateral control system for autonomous vehicles

Abstract

Recently advances in lateral control systems for autonomous vehicles have been focused on achieving high path tracking precision and stable control performance. Building on these efforts, this study designs and implements a lateral control system that addresses limitations such as the absence of path coordinate data, specifically for autonomous vehicles with limited sensor configurations relying solely on GNSS(Global Navigation Satellite System) and IMU(Inertial Measurement Unit). The proposed system consists of three stages: positioning, path tracking, and vehicle control. In the positioning stage, precise positioning is achieved by applying RTK (Real-Time Kinematic) correction and integrating GNSS, IMU data. Additionally, a method is proposed to collect and process path coordinate data in environments where predefined route(path) coordinates are unavailable. In the path tracking stage, an efficient path tracking algorithm based on the Stanley method is implemented and applied to the collected path coordinate data. Finally, in the vehicle control stage, a PID controller is utilized to enhance control precision by minimizing the difference between the vehicle's actual heading and the target heading through adjustments to the steering wheel angle. The experiment was conducted by driving along two target paths within the GIST campus. On the first target path, the Mean Absolute Error(MAE) was recorded at 0.0786m, and the Root Mean Square Error(RMSE) was 0.1140m. Additionally, the average error between the vehicle's target heading and actual heading was recorded as 2.1411°. On the second route, the MAE was 0.0427m, and the RMSE was 0.0802m. The average error between the target heading and actual heading was 2.5000°, demonstrating the performance of the lateral control system. This study demonstrated that stable and efficient path tracking and control performance can be achieved even with limited sensor configurations. It is expected to serve as a foundation for future advanced autonomous driving systems integrating various sensors.