HOPE: Hierarchical Spatial-temporal Network for Occupancy Flow Prediction
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Abstract (arXiv)
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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 23 of its 31 references that are in the map. First posted on arXiv 21 Jun 2022.
Builds on
23 of 31 references.
References · 23
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11 more
- 2020TNT: Target-driveN Trajectory Predictioncited by 347
- 2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
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Built on by
Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 2.
11 papers.
- 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 622
- 2024Solving Motion Planning Tasks with a Scalable Generative Modelcited by 40
- 2023Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Reviewcited by 25
- 2023Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challengecited by 19
- 2024Hybrid-Prediction Integrated Planning for Autonomous Drivingcited by 18
- 2022STrajNet: Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Drivingcited by 8
- 2025CoC-VLA: Delving into Adversarial Domain Transfer for Explainable Autonomous Driving via Chain-of-Causality Visual-Language-Action Modelcited by 7
- 2024Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraintcited by 5
- 2024Learning Temporal Cues by Predicting Objects Move for Multi-camera 3D Object Detectioncited by 1
- 2023The WayHome: Long-term Motion Prediction on Dynamically Scaledcited by 0
- 2025CCLSTM: Coupled Convolutional Long-Short Term Memory Network for Occupancy Flow Forecastingcited by 0
- 2025CoC-VLA: Delving into Adversarial Domain Transfer for Explainable Autonomous Driving via Chain-of-Causality Visual-Language-Action Model25 Nov 2025
- 2025CCLSTM: Coupled Convolutional Long-Short Term Memory Network for Occupancy Flow Forecasting6 Jun 2025
- 2024Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint31 Aug 2024
- 2024Solving Motion Planning Tasks with a Scalable Generative Model3 Jul 2024
- 2024Learning Temporal Cues by Predicting Objects Move for Multi-camera 3D Object Detection2 Apr 2024
- 2024Hybrid-Prediction Integrated Planning for Autonomous Driving4 Feb 2024
- 2023The WayHome: Long-term Motion Prediction on Dynamically Scaled6 Oct 2023
- 2023Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challenge26 Jun 2023
- 2023VAD: Vectorized Scene Representation for Efficient Autonomous Driving21 Mar 2023
- 2023Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review2 Mar 2023
- 2022STrajNet: Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving31 Jul 2022