Path planning module is a key module for autonomous vehicle navigation, which directly affects its operating efficiency and safety. In complex environments with many obstacles, traditional planning algorithms often cannot meet the needs of intelligence, which may lead to problems such as dead zones in unmanned vehicles. This paper proposes a path planning algorithm based on DDQN and combines it with the prioritized experience replay method to solve the problem that traditional path planning algorithms often fall into dead zones. A series of simulation experiment results prove that the path planning algorithm based on DDQN is significantly better than other methods in terms of speed and accuracy, especially the ability to break through dead zones in extreme environments. Research shows that the path planning algorithm based on DDQN performs well in terms of path quality and safety. These research results provide an important reference for the research on automatic navigation of autonomous vehicles.

翻译：路径规划模块是自动驾驶车辆导航的关键模块，直接影响其运行效率与安全性。在障碍物众多的复杂环境中，传统规划算法往往难以满足智能化需求，可能导致无人车陷入死区等问题。本文提出一种基于DDQN的路径规划算法，并结合优先级经验回放方法，以解决传统路径规划算法易陷入死区的问题。系列仿真实验结果表明，基于DDQN的路径规划算法在速度与精度方面显著优于其他方法，尤其在极端环境下突破死区的能力突出。研究表明，基于DDQN的路径规划算法在路径质量与安全性方面表现优异。这些研究成果为自动驾驶车辆自动导航研究提供了重要参考。