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 ${}^{\ast }$ 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 ${}^{\ast }$ algorithms for large-scale map searches, an improved A ${}^{\ast }$ 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. Note to Practitioners—This paper is motivated to provide a quick and responsive path planning and trajectory tracking autonomous method for Ackermann models that can arrive at the destination from the beginning point without encountering any unforeseen obstacles. The current graph search-based A ${}^{\ast }$ algorithm for global path planning is inefficient and prone to too many inflection points; The DWA algorithm, which performs well in real-time, is inefficient for local obstacle avoidance because of its fixed evaluation function, and the conventional PID algorithm has poor tracking accuracy in complex scenes. In order to solve the above problems, this paper proposes an improved A ${}^{\ast }$ algorithm integrating heuristic function, Douglas-Peucker node optimization algorithm and B-spline smoothing algorithm, which effectively improves the global path planning efficiency. A Fuzzy DWA algorithm with two inputs and three outputs is designed for local path planning to improve the stability of dynamic obstacle avoidance, and a B-spline algorithm is subsequently employed to smooth the selected trajectory, thereby enhancing the stability and fluidity of the vehicle’s motion. Finally, the accuracy of trajectory tracking is improved by designing the three key parameters of PID when the fuzzy rule can be dynamically adjusted when facing different scenes. In our follow-up research, we will further optimize the existing algorithms and integrate sensors with higher accuracy for autonomous driving in more complex road environments.