  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.
