Research on Intelligent Algorithms for Path Planning of Driverless Vehicles and their Applications

Driverless technology is a crucial part of future intelligent transportation systems, and path planning is a core technology in driverless systems. This research aims to optimize existing path - planning algorithms and explore new ones. By collecting the latitude and longitude information of Chinese cities from public resources such as Baidu Maps, and after data cleaning, an inter - city distance dataset containing 1,048,574 records was constructed. The research compared the greedy algorithm, dynamic programming, and their derivative algorithms, and selected the Euclidean distance to calculate the distance between points. The results show that there are significant differences in the performance of different algorithms. The Greedy Kruskal algorithm performs relatively well among the greedy algorithm family, and the combination of dynamic programming and the Bellman - Ford algorithm stands out. Based on this, a two - stage intelligent optimization algorithm for driverless vehicles was designed. The first stage uses the Bellman - Ford algorithm for preliminary path planning, and the second stage optimizes the path with the Kruskal algorithm. This algorithm combines the advantages of different algorithms, providing a more effective solution for path planning of driverless vehicles, and is of great significance for promoting the development of autonomous driving technology and constructing intelligent transportation systems.