Research on Path Tracking Control Algorithm for Unmanned Driving Based on Multi-Sensor Fusion

Unmanned driving technology advances so fast that the problem about path tracking control becomes one core problem in an unmanned operating system. The traditional methodology of path tracking control includes PID and LQR, which are simple but not that effective in complicated environment conditions. In recent years, high-order control approaches like model predictive control and sliding mode control have ensured better performance in path tracking, especially while facing run-time variations and nonlinear systems. However, the significant computational intricacy associated with these methodologies limits their popularity in applications involving real-time implementation. Meanwhile, especially in those roads of very complicated composition under heavy weather conditions, the limitation is much stronger regarding the content provided by a single sensor. Arguably, therefore, multi-sensor fusion technology has emerged at this juncture. Integrating multiple sensors-lidar, camera, IMU, and GPS-will improve the accuracy and reliability of micro-path tracking of the system's environmental perception effectively. Among these, the optimization processing of sensor information by fusion algorithms such as the Kalman filter and extended Kalman filter provides quite high stability and path-tracking accuracy. This paper combines multi-sensor fusion technology with advanced control methods to analyze the current status and challenges of unmanned driving path tracking, pointing out the merits and limitations of existing technologies. Furthermore, this will be useful in developing theoretical grounds for the unmanned driving system.