Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments.

Abstract

Vision-based autonomous driving through imitation learning mimics the behavior of human drivers by mapping driver view images to driving actions. This article shows that performance can be enhanced via the use of eye gaze. Previous research has shown that observing an expert's gaze patterns can be beneficial for novice human learners. We show here that neural networks can also benefit. We trained a conditional generative adversarial network to estimate human gaze maps accurately from driver-view images. We describe two approaches to integrating gaze information into imitation networks: eye gaze as an additional input and gaze modulated dropout. Both significantly enhance generalization to unseen environments in comparison with a baseline vanilla network without gaze, but gaze-modulated dropout performs better. We evaluated performance quantitatively on both single images and in closed-loop tests, showing that gaze modulated dropout yields the lowest prediction error, the highest success rate in overtaking cars, the longest distance between infractions, lowest epistemic uncertainty, and improved data efficiency. Using Grad-CAM, we show that gaze modulated dropout enables the network to concentrate on task-relevant areas of the image.