  Deep neural perception and control networks are likely to be a key component
of self-driving vehicles. These models need to be explainable - they should
provide easy-to-interpret rationales for their behavior - so that passengers,
insurance companies, law enforcement, developers etc., can understand what
triggered a particular behavior. Here we explore the use of visual
explanations. These explanations take the form of real-time highlighted regions
of an image that causally influence the network's output (steering control).
Our approach is two-stage. In the first stage, we use a visual attention model
to train a convolution network end-to-end from images to steering angle. The
attention model highlights image regions that potentially influence the
network's output. Some of these are true influences, but some are spurious. We
then apply a causal filtering step to determine which input regions actually
influence the output. This produces more succinct visual explanations and more
accurately exposes the network's behavior. We demonstrate the effectiveness of
our model on three datasets totaling 16 hours of driving. We first show that
training with attention does not degrade the performance of the end-to-end
network. Then we show that the network causally cues on a variety of features
that are used by humans while driving.
