Application of Genetic Algorithm to Path Planning Problem of Automatic Navigation Parking Spaces in Parking Lots

With the accelerated process of urbanization, traffic congestion and parking difficulties have gradually become key factors affecting the quality of life of urban residents. To address this challenge, this study proposes an intelligent parking lot navigation and optimal parking spot path planning method based on genetic algorithm. This method fully utilizes the superior adaptability of genetic algorithm, can flexibly adapt to changes in the parking lot environment, search for the optimal parking spot, thereby shortening the distance of vehicle driving in the parking lot, reducing traffic congestion, and saving time for finding parking spots. In this study, we first constructed a comprehensive parking lot model, including parking spaces, occupied parking spaces, entrances and exits, and other relevant parameters. Next, we designed and implemented a genetic algorithm, including individual generation, fitness function, crossover operation, mutation operation, and genetic optimization process. To demonstrate the practicality of the algorithm, we used a Tkinter graphical user interface to simulate the parking lot environment and present the path planning results. After experimental verification, the proposed intelligent parking lot navigation and optimal parking spot path planning method based on genetic algorithm in this study performed well in the driving performance of the parking lot, effectively solving the problem of parking difficulties and improving the efficiency of urban traffic operation.