潜在深層強化学習による解釈可能な都市自動運転
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
逐次潜在環境モデルを強化学習と同時に学習し、意味的バードアイマスクを生成することで、複雑な都市シナリオでの自動運転方策を解釈可能にした研究。
著者: Jianyu Chen, Shengbo Eben Li, Masayoshi Tomizuka
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to new scenarios and easier to generalize at scale. However, existing end-to-end approaches are often lack of interpretability, and can only deal with simple driving tasks like lane keeping. In this paper, we propose an interpretable deep reinforcement learning method for end-to-end autonomous driving, which is able to handle complex urban scenarios. A sequential latent environment model is introduced and learned jointly with the reinforcement learning process. With this latent model, a semantic birdeye mask can be generated, which is enforced to connect with a certain intermediate property in today's modularized framework for the purpose of explaining the behaviors of learned policy. The latent space also significantly reduces the sample complexity of reinforcement learning. Comparison tests with a simulated autonomous car in CARLA show that the performance of our method in urban scenarios with crowded surrounding vehicles dominates many baselines including DQN, DDPG, TD3 and SAC. Moreover, through masked outputs, the learned policy is able to provide a better explanation of how the car reasons about the driving environment. The codes and videos of this work are available at our github repo and project website.
