Intelligent Vehicle Path Optimization Based On Astar Algorithm

This paper focuses on the path optimization problem of intelligent vehicles. After comprehensively comparing the advantages and disadvantages of traditional path planning algorithms, the Astar algorithm with better comprehensive performance is selected as the basis of path optimization, and the Astar algorithm model is improved and optimized in terms of reducing traversal nodes and optimizing redundant turning points of the path. In order to verify the actual effect of the improved Astar algorithm in path optimization, this paper selects urban map and vehicle kinematics model as the application objects of the algorithm, and verifies the effect of global path planning and local path planning respectively. The verification results show that the improved Astar algorithm is feasible in the actual path planning, and the path is better, which can provide a theoretical basis for the further research of the subsequent Astar algorithm.