  Imitation learning by behavioral cloning is a prevalent method that has
achieved some success in vision-based autonomous driving. The basic idea behind
behavioral cloning is to have the neural network learn from observing a human
expert's behavior. Typically, a convolutional neural network learns to predict
the steering commands from raw driver-view images by mimicking the behaviors of
human drivers. However, there are other cues, such as gaze behavior, available
from human drivers that have yet to be exploited. Previous researches have
shown that novice human learners can benefit from observing experts' gaze
patterns. We present here that deep neural networks can also profit from this.
We propose a method, gaze-modulated dropout, for integrating this gaze
information into a deep driving network implicitly rather than as an additional
input. Our experimental results demonstrate that gaze-modulated dropout
enhances the generalization capability of the network to unseen scenes.
Prediction error in steering commands is reduced by 23.5% compared to uniform
dropout. Running closed loop in the simulator, the gaze-modulated dropout net
increased the average distance travelled between infractions by 58.5%.
Consistent with these results, the gaze-modulated dropout net shows lower model
uncertainty.
