  Autonomous driving is a multi-task problem requiring a deep understanding of
the visual environment. End-to-end autonomous systems have attracted increasing
interest as a method of learning to drive without exhaustively programming
behaviours for different driving scenarios. When humans drive, they rely on a
finely tuned sensory system which enables them to quickly acquire the
information they need while filtering unnecessary details. This ability to
identify task-specific high-interest regions within an image could be
beneficial to autonomous driving agents and machine learning systems in
general. To create a system capable of imitating human gaze patterns and visual
attention, we collect eye movement data from human drivers in a virtual reality
environment. We use this data to train deep neural networks predicting where
humans are most likely to look when driving. We then use the outputs of this
trained network to selectively mask driving images using a variety of masking
techniques. Finally, autonomous driving agents are trained using these masked
images as input. Upon comparison, we found that a dual-branch architecture
which processes both raw and attention-masked images substantially outperforms
all other models, reducing error in control signal predictions by 25.5\%
compared to a standard end-to-end model trained only on raw images.
