A Review - Optimal Path Finding in Grid Parking

Abstract: Efficient parking management has become a critical concern in modern urban environments due to rapid vehicle growth and limited infrastructure. This review paper examines the current advancements in grid-based parking navigation systems, focusing on the use of A* (A-star) algorithm for optimal path planning. The paper provides a comprehensive analysis of various pathfinding techniques, including Dijkstra, Breadth-First Search, and heuristic-driven methods, highlighting their strengths, limitations, and applicability to intelligent parking systems. Particular attention is given to how A* integrates heuristic evaluation with actual movement cost to efficiently determine the shortest path in environments with static and dynamic obstacles. Additionally, this review discusses recent Python-based implementations and simulation approaches for visualizing and evaluating pathfinding performance in gridmodeled parking lots. The insights gained from this study can guide future research in autonomous vehicle navigation, smart parking solutions, and real-time traffic management systems, providing a foundation for the development of more efficient and scalable intelligent transportation frameworks.