  We propose and demonstrate a framework called perception as prediction for
autonomous driving that uses general value functions (GVFs) to learn
predictions. Perception as prediction learns data-driven predictions relating
to the impact of actions on the agent's perception of the world. It also
provides a data-driven approach to predict the impact of the anticipated
behavior of other agents on the world without explicitly learning their policy
or intentions. We demonstrate perception as prediction by learning to predict
an agent's front safety and rear safety with GVFs, which encapsulate
anticipation of the behavior of the vehicle in front and in the rear,
respectively. The safety predictions are learned through random interactions in
a simulated environment containing other agents. We show that these predictions
can be used to produce similar control behavior to an LQR-based controller in
an adaptive cruise control problem as well as provide advanced warning when the
vehicle behind is approaching dangerously. The predictions are compact
policy-based predictions that support prediction of the long term impact on
safety when following a given policy. We analyze two controllers that use the
learned predictions in a racing simulator to understand the value of the
predictions and demonstrate their use in the real-world on a Clearpath Jackal
robot and an autonomous vehicle platform.
