A Dynamic Path Planning and Tracking Control of Autonomous Vehicles: An Integrated Approach Using Improved A * , Fuzzy DWA, and Fuzzy PID

This paper presents a systematic investigation into path planning and trajectory tracking for autonomous vehicles. By integrating an improved A* algorithm, a fuzzy dynamic window approach, and a Fuzzy PID control strategy, the proposed method enables effective driving of an autonomous vehicle. Firstly, in the global path planning phase, to address the issues of low computational efficiency and suboptimal path quality in traditional A* algorithms for large-scale map searches, an improved A* algorithm incorporating an enhanced heuristic function, redundant node removal strategy, and path smoothing approach is introduced, significantly increasing search efficiency and optimizing path quality. Secondly, in the local path planning phase, the dynamic adjustment of vehicle speed and steering is achieved by combining fuzzy logic control with the dynamic window approach. This allows for smooth obstacle avoidance in dynamic environments. Furthermore, a path smoothing algorithm is integrated to refine the generated trajectory, ensuring its continuity and smoothness. Finally, a Fuzzy PID control algorithm is integrated into the trajectory tracking controller. By introducing fuzzy logic, the PID parameters are adaptively adjusted to ensure precise vehicle following of the planned path, improving path tracking stability and response speed. The proposed method is validated and evaluated in a variety of complex road scenarios using a real vehicle based on ROS. The simulation and real-world experimental results clearly illustrate that the proposed method achieves substantially better performance than conventional approaches with regard to path planning efficiency, obstacle avoidance success rate, and path smoothness.