  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.
