Autonomous driving technology can improve travel safety and shorten travel time. Reasonable route planning directly affects the safety and comfort of autonomous vehicles. To address the issues of low search efficiency and non-compliance with vehicle kinematic constraints in traditional route planning algorithms for autonomous vehicles, improvements have been made. By replacing the local target points of the dynamic window method with key nodes of the improved ant colony algorithm for route planning, and adding a target distance evaluation sub-function to the evaluation function of the dynamic window method, the efficiency and smoothness of route planning are improved. Additionally, a path replanning method is used to address global path failure, allowing vehicles to escape obstacles and meet safety requirements for route planning. The improved ant colony algorithm uses the location information of start and end points to distribute initial pheromones unevenly, reducing time consumption in the initial search phase. By maintaining the global optimal path and increasing the concentration of pheromones on excellent local paths, the information update mechanism is optimized to accelerate path exploration efficiency. Secondary optimization of planned paths reduces redundancy in nodes and turning points, thus reducing path length. Simulation results show that the integrated algorithm proposed in this paper provides better performance in terms of distance, smoothness, and convergence compared to traditional route planning algorithms, and meets the requirements for safe driving of autonomous vehicles.