To address the issues of low search efficiency,long distance,and non-smooth paths in traditional path planning algorithms for autonomous vehicles,this study proposes an improvement by using key nodes of the optimized ant colony algorithm to replace the local target points in the dynamic window approach.Additionally,a target distance evaluation sub-function is incorporated into the dynamic window approach's evaluation function to enhance the efficiency and smoothness of path planning.Furthermore,a path decision-making method is employed to solve the problem of global path failure,enabling the vehicle to avoid obstacles and meet safety requirements of path planning.The improved ant colony algorithm utilizes the positional information of the start and end points to create an uneven initial pheromone distribution,thereby reducing time consumption during the initial search phase.By maintaining the global optimal paths and enhancing the pheromone concentration of excellent local paths,the pheromone update mechanism is optimized to speed up path exploration efficiency.The planned path is further optimized to reduce redundancy in nodes and turning points,thereby shortening path length.Simulation results show that compared to traditional path planning algorithms,the proposed integrated algorithm achieves better performance in terms of distance,smoothness,and convergence,aligning with the safety requirements for autonomous vehicle operation.