Prioritized experience replay-based DDQN for Unmanned Vehicle Path Planning

TL;DR

This paper introduces a DDQN-based path planning algorithm with prioritized experience replay, significantly improving autonomous vehicle navigation in complex environments by avoiding dead zones and enhancing speed and accuracy.

Contribution

It presents a novel combination of DDQN and prioritized experience replay for effective path planning in autonomous vehicles, especially in challenging environments.

Findings

Outperforms traditional methods in speed and accuracy

Effectively avoids dead zones in complex environments

Maintains high path quality and safety

Abstract

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.