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

arXiv: https://arxiv.org/abs/1907.04728 · HTML: https://mlsanity.com/paper/1907.04728/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 10 Jul 2019.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Built on by (most cited first, 1 of 1)

- [Estimating Central, Peripheral, and Temporal Visual Contributions to Human Decision Making in Atari Games](https://mlsanity.com/paper/2604.04439/) (2026)

## Abstract (arXiv)

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.