協調性を考慮した強化学習による密集交通での合流
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
他車の協調度合いに関する信念を保持しながら強化学習で合流行動を学習し、オンライン計画法よりデッドロックを減らした研究。
著者: Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the maneuver. Human drivers leverage the cooperation of other drivers to avoid such deadlock situations and convince others to change their behavior. Decision making algorithms must reason about the interaction with other drivers and anticipate a broad range of driver behaviors. In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels. We enhanced the performance of traditional reinforcement learning algorithms by maintaining a belief over the level of cooperation of other drivers. We show that our agent successfully learns how to navigate a dense merging scenario with less deadlocks than with online planning methods.
