mlsanityData to 7 Oct 2026

Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments

arXiv 2209.13172 ↗First posted 27 Sep 2022cs.RO cs.CVcited by 17

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Abstract (arXiv)

Detection and segmentation of moving obstacles, along with prediction of the future occupancy states of the local environment, are essential for autonomous vehicles to proactively make safe and informed decisions. In this paper, we propose a framework that integrates the two capabilities together using deep neural network architectures. Our method first detects and segments moving objects in the scene, and uses this information to predict the spatiotemporal evolution of the environment around autonomous vehicles. To address the problem of direct integration of both static-dynamic object segmentation and environment prediction models, we propose using occupancy-based environment representations across the whole framework. Our method is validated on the real-world Waymo Open Dataset and demonstrates higher prediction accuracy than baseline methods.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 27 Sep 2022.

Builds on

14 of 21 references.

References · 14

  1. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  2. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  3. 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
  4. 2016Deep Predictive Coding Networks for Video Prediction and Unsupervised Learningcited by 203
  5. 2018PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learningcited by 122
  6. 2018Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamicscited by 91
  7. 2021Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Datacited by 64
  8. 2018Long-Term Occupancy Grid Prediction Using Recurrent Neural Networkscited by 29
  9. 2018Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networkscited by 23
  10. 2019Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learningcited by 18
  11. 2016A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Applicationcited by 17
  12. 2020Double-Prong ConvLSTM for Spatiotemporal Occupancy Prediction in Dynamic Environmentscited by 13
2 more
  1. 2020Attention Augmented ConvLSTM for Environment Predictioncited by 12
  2. 2016Robust Moving Objects Detection in Lidar Data Exploiting Visual Cuescited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 6; 2025: 3; 2026: 2.

17 papers.

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  1. 2024Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Drivingcited by 67
  2. 2025A Survey of World Models for Autonomous Drivingcited by 60
  3. 2023Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applicationscited by 50
  4. 2025UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Drivingcited by 27
  5. 2024CMP: Cooperative Motion Prediction with Multi-Agent Communicationcited by 25
  6. 2023Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypointscited by 12
  7. 2022Targeted Adversarial Attacks against Neural Network Trajectory Predictorscited by 11
  8. 2024DUFOMap: Efficient Dynamic Awareness Mappingcited by 11
  9. 2023A Dynamic Points Removal Benchmark in Point Cloud Mapscited by 8
  10. 2024Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecastingcited by 7
  11. 2024Self-supervised Multi-future Occupancy Forecasting for Autonomous Drivingcited by 7
  12. 2025LEGO-Motion: Learning-Enhanced Grids with Occupancy Instance Modeling for Class-Agnostic Motion Predictioncited by 6
5 more
  1. 2026ForecastOcc: Vision-based Semantic Occupancy Forecastingcited by 6
  2. 2022LOPR: Latent Occupancy PRediction using Generative Modelscited by 4
  3. 2023Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Mapscited by 3
  4. 2024Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Mapscited by 2
  5. 2026Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Mapscited by 0