A Vehicle Routing Planning Algorithms Based on Improved Ant Colony Strategy

In the context of the rapid development of intelligent transportation systems, traditional path planning algorithms are facing severe challenges, especially in meeting the requirements of autonomous driving technology for prediction timeliness and accuracy. In order to solve the problem that the ant colony algorithm is easy to fall into the local optimal solution and the search efficiency is not high in path planning, an improved ant colony algorithm is proposed. The improvement measures include the update of the cost function and the optimization of the pheromone update mechanism, aiming to improve the adaptability of the algorithm in complex environments and the efficiency of path planning. In this study, simulation experiments were designed to compare the performance of the traditional ant colony algorithm and the improved algorithm by constructing a test map that simulated a complex environment. The experimental results reveal that the optimized algorithm has a significant improvement compared with the traditional algorithm in terms of path length, driving safety, comfort and computing efficiency. Especially when dealing with path planning problems in dynamic environments, the improved algorithm shows higher robustness and better search efficiency. Through quantitative experimental indicators and qualitative analysis, this paper confirms the application potential of the improved ant colony algorithm in intelligent transportation systems, especially emphasizing its advantages in ensuring the quality of path planning solutions and reducing the computation time. The research results provide a new solution for path planning in autonomous driving technology, which is of great significance for realizing a more intelligent traffic management system. Future work will focus on the further optimization of the algorithm and explore its performance in a wider range of real-world application scenarios to promote its wide deployment in the field of intelligent transportation.