深層強化学習による遮蔽された交差点の自動運転ナビゲーション
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
深層強化学習を用いて信号のない交差点を安全に通過する方策を学習し、従来のヒューリスティック手法を上回る性能を示すとともに、遮蔽時には能動センシング行動を学習できることを示した。
著者: David Isele, Reza Rahimi, Akansel Cosgun, Kaushik Subramanian, Kikuo Fujimura
分類: cs.AI, cs.RO
原文アブストラクト
Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.
