# Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1812.09395 · HTML: https://mlsanity.com/paper/1812.09395/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 24 of 24)

- [End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps](https://mlsanity.com/paper/2203.16910/) (2022)
- [ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton](https://mlsanity.com/paper/2112.01044/) (2021)
- [Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2108.00238/) (2021)
- [Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments](https://mlsanity.com/paper/2209.13172/) (2022)
- [Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2506.05997/) (2025)
- [Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning](https://mlsanity.com/paper/2205.03212/) (2022)
- [Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving](https://mlsanity.com/paper/2407.21126/) (2024)
- [Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map](https://mlsanity.com/paper/2103.01039/) (2021)
- [Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2107.03600/) (2021)
- [Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud](https://mlsanity.com/paper/2203.00138/) (2022)
- [LOPR: Latent Occupancy PRediction using Generative Models](https://mlsanity.com/paper/2210.01249/) (2022)
- [Spatial Imagination With Semantic Cognition for Mobile Robots](https://mlsanity.com/paper/2104.03638/) (2021)
- [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)
- [Vehicle trajectory prediction in top-view image sequences based on deep learning method](https://mlsanity.com/paper/2102.01749/) (2021)
- [Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2407.15675/) (2024)
- [Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation](https://mlsanity.com/paper/2212.13676/) (2022)
- [Deep Occupancy-Predictive Representations for Autonomous Driving](https://mlsanity.com/paper/2303.04218/) (2023)
- [ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles](https://mlsanity.com/paper/2107.08690/) (2021)
- [Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction](https://mlsanity.com/paper/2306.16306/) (2023)
- [Does Unpredictability Influence Driving Behavior?](https://mlsanity.com/paper/2307.15287/) (2023)
- [Invariant-based Mapping of Space During General Motion of an Observer](https://mlsanity.com/paper/2311.11130/) (2023)
- [Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2602.07938/) (2026)
- [ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning](https://mlsanity.com/paper/2606.16558/) (2026)

## Abstract (arXiv)

We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that accurate multi-step prediction of the drivable space can efficiently improve path planning and navigation resulting in safe, comfortable and optimum paths in autonomous driving. We train a variety of Recurrent Neural Network (RNN) based architectures on the OGM sequences from the KITTI dataset. The results demonstrate significant improvement of the prediction accuracy using our proposed difference learning method, incorporating motion related features, over the state of the art. We remove the egomotion from the OGM sequences by transforming them into a common frame. Although in the transformed sequences the KITTI dataset is heavily biased toward static objects, by learning the difference between subsequent OGMs, our proposed method provides accurate prediction over both the static and moving objects.