On dynamic path planning based on the DBSCAN-AGA algorithm

With the advancement of intelligent vehicles, unmanned driving technology has achieved significant progress, particularly in low-speed park settings. However, challenges arise in the park connections due to the dynamic variations of passengers and the complexities of road conditions, making it difficult to implement dynamic path planning for traffic demand distributions. This paper introduces a path planning algorithm based on the adaptive genetic algorithm (AGA) for connecting vehicles on variable routes. This approach involves constructing an origin-destination (OD) matrix based on passengers' origin and destination points, and incorporates the density-based spatial clustering of applications with noise (DBSCAN) to reassign traffic demand by adjusting routing of connecting vehicles according to traffic demand and road network traffic conditions. The obtained results validate the effectiveness of the proposed method, demonstrating that the DBSCAN-AGA algorithm exhibits strong robustness and reliability in dynamic environment path planning.