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Vision-based autonomous driving through imitation learning mimics thebehaviors of human drivers by training on pairs of data of raw driver-viewimages and actions. However, there are other cues, e.g. gaze behavior,available from human drivers that have yet to be exploited. Previous researchhas shown that novice human learners can benefit from observing experts' gazepatterns. We show here that deep neural networks can also benefit from this. Wedemonstrate different approaches to integrating gaze information into imitationnetworks. Our results show that the integration of gaze information improvesthe generalization performance of networks to unseen environments.

Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments