# Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

arXiv: https://arxiv.org/abs/1705.08781 · HTML: https://mlsanity.com/paper/1705.08781/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2017.

## Built on by (most cited first, 30 of 30)

- [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://mlsanity.com/paper/2101.06806/) (2021)
- [IntentNet: Learning to Predict Intention from Raw Sensor Data](https://mlsanity.com/paper/2101.07907/) (2021)
- [Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders](https://mlsanity.com/paper/2206.09900/) (2022)
- [Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2110.04076/) (2021)
- [Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey](https://mlsanity.com/paper/2305.04691/) (2023)
- [Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving](https://mlsanity.com/paper/2109.01510/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving](https://mlsanity.com/paper/2404.01486/) (2024)
- [Learning to Drive via Asymmetric Self-Play](https://mlsanity.com/paper/2409.18218/) (2024)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [STrajNet: Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving](https://mlsanity.com/paper/2208.00394/) (2022)
- [OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment](https://mlsanity.com/paper/2404.02263/) (2024)
- [Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm](https://mlsanity.com/paper/2107.12940/) (2021)
- [Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning](https://mlsanity.com/paper/2205.03212/) (2022)
- [Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting](https://mlsanity.com/paper/2101.02385/) (2021)
- [Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map](https://mlsanity.com/paper/2103.01039/) (2021)
- [Towards Learning Generalizable Driving Policies from Restricted Latent Representations](https://mlsanity.com/paper/2111.03688/) (2021)
- [Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud](https://mlsanity.com/paper/2203.00138/) (2022)
- [Vehicle trajectory prediction in top-view image sequences based on deep learning method](https://mlsanity.com/paper/2102.01749/) (2021)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [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)
- [The Autonomous Siemens Tram](https://mlsanity.com/paper/2102.04034/) (2021)
- [Decentralized cooperative perception for autonomous vehicles: Learning to value the unknown](https://mlsanity.com/paper/2301.01250/) (2022)
- [Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time](https://mlsanity.com/paper/2306.06126/) (2023)
- [Does Unpredictability Influence Driving Behavior?](https://mlsanity.com/paper/2307.15287/) (2023)
- [GAD-Generative Learning for HD Map-Free Autonomous Driving](https://mlsanity.com/paper/2405.00515/) (2024)
- [Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks](https://mlsanity.com/paper/2409.11373/) (2024)
- [A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2505.07855/) (2025)
- [An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios](https://mlsanity.com/paper/2509.13132/) (2025)

## Abstract (arXiv)

Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation.