Automatic Car Parking Based on Petri Net, PFA Algorithm, and Fuzzy System

This paper presents the design and implementation of a hybrid intelligent system aimed at optimizing vehicle parking through efficient path planning. With the increasing demand for intelligent transportation systems, optimizing parking spaces and vehicle movement within parking areas has become a crucial research focus. The proposed system integrates multiple computational techniques to enhance the accuracy and efficiency of automatic parking. The Path Finding Algorithm (PFA) is employed to identify potential parking locations based on predefined constraints and real-time data. Unlike conventional approaches that rely on static flowcharts for parking path planning, this study utilizes Petri nets, which offer a more dynamic and structured framework for modeling alternative parking paths, particularly in global coordinate systems. This method enables adaptive and flexible decision-making in response to varying parking scenarios. To ensure precise maneuverability along the optimized path, a fuzzy logic control system is implemented, allowing the vehicle to adapt its movements in real time based on environmental factors and space constraints. The effectiveness of the proposed system is validated through numerical simulations and experimental studies, demonstrating its capability to improve both parking efficiency and vehicle positioning accuracy. Results indicate that the hybrid integration of PFA, Petri nets, and fuzzy logic significantly enhances the automation and optimization of the vehicle parking process, offering a robust, adaptive, and intelligent parking solution for modern transportation systems.

کلیدواژه ها:

Path Optimization ، PFA Algorithm ، Petri Nets ، Fuzzy inference system ، Automatic Parking