HOPE: Hierarchical Spatial-temporal Network for Occupancy Flow Prediction

yihan hu, Wenxin Shao, Bo Jiang, Jiajie Chen, Siqi Chai, Zhening Yang, Jingyu Qian, Helong Zhou, Qiang Liu

Abstract: In this report, we introduce our solution to the Occupancy and Flow Prediction challenge in the Waymo Open Dataset Challenges at CVPR 2022. 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.