  Convolutions on monocular dash cam videos capture spatial invariances in the
image plane but do not explicitly reason about distances and depth. We propose
a simple transformation of observations into a bird's eye view, also known as
plan view, for end-to-end control. We detect vehicles and pedestrians in the
first person view and project them into an overhead plan view. This
representation provides an abstraction of the environment from which a deep
network can easily deduce the positions and directions of entities.
Additionally, the plan view enables us to leverage advances in 3D object
detection in conjunction with deep policy learning. We evaluate our monocular
plan view network on the photo-realistic Grand Theft Auto V simulator. A
network using both a plan view and front view causes less than half as many
collisions as previous detection-based methods and an order of magnitude fewer
collisions than pure pixel-based policies.
