  Deep neural networks are a key component of behavior prediction and motion
generation for self-driving cars. One of their main drawbacks is a lack of
transparency: they should provide easy to interpret rationales for what
triggers certain behaviors. We propose an architecture called Attentional
Bottleneck with the goal of improving transparency. Our key idea is to combine
visual attention, which identifies what aspects of the input the model is
using, with an information bottleneck that enables the model to only use
aspects of the input which are important. This not only provides sparse and
interpretable attention maps (e.g. focusing only on specific vehicles in the
scene), but it adds this transparency at no cost to model accuracy. In fact, we
find slight improvements in accuracy when applying Attentional Bottleneck to
the ChauffeurNet model, whereas we find that the accuracy deteriorates with a
traditional visual attention model.
