  Predicting future behaviors of road agents is a key task in autonomous
driving. While existing models have demonstrated great success in predicting
marginal agent future behaviors, it remains a challenge to efficiently predict
consistent joint behaviors of multiple agents. Recently, the occupancy flow
fields representation was proposed to represent joint future states of road
agents through a combination of occupancy grid and flow, which supports
efficient and consistent joint predictions. In this work, we propose a novel
occupancy flow fields predictor to produce accurate occupancy and flow
predictions, by combining the power of an image encoder that learns features
from a rasterized traffic image and a vector encoder that captures information
of continuous agent trajectories and map states. The two encoded features are
fused by multiple attention modules before generating final predictions. Our
simple but effective model ranks 3rd place on the Waymo Open Dataset Occupancy
and Flow Prediction Challenge, and achieves the best performance in the
occluded occupancy and flow prediction task.
