  Motion planning for safe autonomous driving requires learning how the
environment around an ego-vehicle evolves with time. Ego-centric perception of
driveable regions in a scene not only changes with the motion of actors in the
environment, but also with the movement of the ego-vehicle itself.
Self-supervised representations proposed for large-scale planning, such as
ego-centric freespace, confound these two motions, making the representation
difficult to use for downstream motion planners. In this paper, we use
geometric occupancy as a natural alternative to view-dependent representations
such as freespace. Occupancy maps naturally disentangle the motion of the
environment from the motion of the ego-vehicle. However, one cannot directly
observe the full 3D occupancy of a scene (due to occlusion), making it
difficult to use as a signal for learning. Our key insight is to use
differentiable raycasting to "render" future occupancy predictions into future
LiDAR sweep predictions, which can be compared with ground-truth sweeps for
self-supervised learning. The use of differentiable raycasting allows occupancy
to emerge as an internal representation within the forecasting network. In the
absence of groundtruth occupancy, we quantitatively evaluate the forecasting of
raycasted LiDAR sweeps and show improvements of upto 15 F1 points. For
downstream motion planners, where emergent occupancy can be directly used to
guide non-driveable regions, this representation relatively reduces the number
of collisions with objects by up to 17% as compared to freespace-centric motion
planners.
