不確実性を考慮した視覚ベース自動運転の展開
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
テスト画像を学習ドメインに変換する際に確率的生成器で多様な画像を作り、不確実性を考慮した模倣学習ポリシーで最も不確実性の低い安全な行動を選択する手法を提案した。
著者: Lei Tai, Peng Yun, Yuying Chen, Congcong Liu, Haoyang Ye, Ming Liu
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually directly applied without considering the uncertainty of the input data. Such kind of policies may bring dramatical damage when applied in the real world. In this paper, we follow the recent real-to-sim pipeline by translating the testing world image back to the training domain when using the trained policy. In the translating process, a stochastic generator is used to generate various images stylized under the training domain randomly or directionally. Based on those translated images, the trained uncertainty-aware imitation learning policy would output both the predicted action and the data uncertainty motivated by the aleatoric loss function. Through the uncertainty-aware imitation learning policy, we can easily choose the safest one with the lowest uncertainty among the generated images. Experiments in the Carla navigation benchmark show that our strategy outperforms previous methods, especially in dynamic environments.
