行動クローニングとPPOによる経路追従と静的障害物回避の経路計画
Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging
自動運転の経路計画において、行動クローニングで経路追従を、PPOで静的障害物の回避を学習し、横方向オフセットを出力して参照経路を修正する手法を提案した。
著者: Mingyan Zhou, Biao Wang, Tian Tan, Xiatao Sun
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
