  The goal of our work is to use visual attention to enhance autonomous driving
performance. We present two methods of predicting visual attention maps. The
first method is a supervised learning approach in which we collect eye-gaze
data for the task of driving and use this to train a model for predicting the
attention map. The second method is a novel unsupervised approach where we
train a model to learn to predict attention as it learns to drive a car.
Finally, we present a comparative study of our results and show that the
supervised approach for predicting attention when incorporated performs better
than other approaches.
