Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging
Year: 2024
Type: article
Abstract: In autonomous driving, end-to-end methods utilizing Imitation Learning (IL) and Reinforcement Learning (RL) are becoming more and more common. However, they do not involve explicit reasoning like classic robotics workflow and planning with horizons, resulting in strategies implicit and myopic. In this paper, we introduce a path planning method that uses Behavioral Cloning (BC) for path-tracking and Proximal Policy Optimization (PPO) for static obstacle nudging. It outputs lateral offset values to adjust the given reference waypoints and performs modified path for different controllers. Experimental results show that the algorithm can do path following that mimics the expert performance of path-tracking controllers, and avoid collision to fixed obstacles. The method makes a good attempt at planning with learning-based methods in path planning problems of autonomous driving.
DOI: 10.54097/189b3413
Authors: Mingyan Zhou, Biao Wang, Tian Tan, Xiatao Sun
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: University of Michigan
OpenAlex ID: W4403986824