  In this report, we introduce our solution to the Occupancy and Flow
Prediction challenge in the Waymo Open Dataset Challenges at CVPR 2022, which
ranks 1st on the leaderboard. We have developed a novel hierarchical
spatial-temporal network featured with spatial-temporal encoders, a multi-scale
aggregator enriched with latent variables, and a recursive hierarchical 3D
decoder. We use multiple losses including focal loss and modified flow trace
loss to efficiently guide the training process. Our method achieves a
Flow-Grounded Occupancy AUC of 0.8389 and outperforms all the other teams on
the leaderboard.

Computer Vision and Pattern Recognition