視線情報で模倣学習ネットワークの未知環境への汎化を強化
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments
自動運転の模倣学習に人間の視線データを組み込み、未見環境への汎化性能が向上することを示した研究。
著者: Congcong Liu, Yuying Chen, Lei Tai, Ming Liu, Bertram Shi
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Vision-based autonomous driving through imitation learning mimics the behaviors of human drivers by training on pairs of data of raw driver-view images and actions. However, there are other cues, e.g. gaze behavior, available from human drivers that have yet to be exploited. Previous research has shown that novice human learners can benefit from observing experts' gaze patterns. We show here that deep neural networks can also benefit from this. We demonstrate different approaches to integrating gaze information into imitation networks. Our results show that the integration of gaze information improves the generalization performance of networks to unseen environments.
