  Predicting the possible future behaviors of vehicles that drive on shared
roads is a crucial task for safe autonomous driving. Many existing approaches
to this problem strive to distill all possible vehicle behaviors into a
simplified set of high-level actions. However, these action categories do not
suffice to describe the full range of maneuvers possible in the complex road
networks we encounter in the real world. To combat this deficiency, we propose
a new method that leverages the mapped road topology to reason over possible
goals and predict the future spatial occupancy of dynamic road actors. We show
that our approach is able to accurately predict future occupancy that remains
consistent with the mapped lane geometry and naturally captures multi-modality
based on the local scene context while also not suffering from the mode
collapse problem observed in prior work.
