# Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps

arXiv: https://arxiv.org/abs/2407.15675 · HTML: https://mlsanity.com/paper/2407.15675/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://mlsanity.com/paper/2101.06806/) (2021)
- [PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2103.09504/) (2021)
- [Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions](https://mlsanity.com/paper/1906.08945/) (2019)
- [MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps](https://mlsanity.com/paper/2003.06754/) (2020)
- [Occupancy Flow Fields for Motion Forecasting in Autonomous Driving](https://mlsanity.com/paper/2203.03875/) (2022)
- [Probabilistic Future Prediction for Video Scene Understanding](https://mlsanity.com/paper/2003.06409/) (2020)
- [Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks](https://mlsanity.com/paper/1809.03782/) (2018)
- [Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks](https://mlsanity.com/paper/1812.09395/) (2018)
- [Recurrent Flow-Guided Semantic Forecasting](https://mlsanity.com/paper/1809.08318/) (2018)
- [Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments](https://mlsanity.com/paper/2209.13172/) (2022)
- [Attention Augmented ConvLSTM for Environment Prediction](https://mlsanity.com/paper/2010.09662/) (2020)
- [Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning](https://mlsanity.com/paper/2205.03212/) (2022)
- [Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks](https://mlsanity.com/paper/2301.04454/) (2023)
- [Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps](https://mlsanity.com/paper/2308.04303/) (2023)

## Built on by (most cited first, 2 of 2)

- [Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2602.07938/) (2026)
- [CorrelationFlow: A Training-Free Geometric Approach for LiDAR Scene Flow Estimation](https://mlsanity.com/paper/2607.29237/) (2026)

## Abstract (arXiv)

Accurate prediction of driving scenes is essential for road safety and autonomous driving. Occupancy Grid Maps (OGMs) are commonly employed for scene prediction due to their structured spatial representation, flexibility across sensor modalities and integration of uncertainty. Recent studies have successfully combined OGMs with deep learning methods to predict the evolution of scene and learn complex behaviours. These methods, however, do not consider prediction of flow or velocity vectors in the scene. In this work, we propose a novel multi-task framework that leverages dynamic OGMs and semantic information to predict both future vehicle semantic grids and the future flow of the scene. This incorporation of semantic flow not only offers intermediate scene features but also enables the generation of warped semantic grids. Evaluation on the real-world NuScenes dataset demonstrates improved prediction capabilities and enhanced ability of the model to retain dynamic vehicles within the scene.