Differentiable Raycasting for Self-supervised Occupancy Forecasting

Understand

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.