# Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks

arXiv: https://arxiv.org/abs/2301.04454 · HTML: https://mlsanity.com/paper/2301.04454/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning](https://mlsanity.com/paper/1605.08104/) (2016)
- [PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2103.09504/) (2021)
- [A Review on Deep Learning Techniques for Video Prediction](https://mlsanity.com/paper/2004.05214/) (2020)
- [Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning](https://mlsanity.com/paper/2104.00924/) (2021)
- [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019)
- [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)
- [Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning](https://mlsanity.com/paper/1904.12374/) (2019)
- [Double-Prong ConvLSTM for Spatiotemporal Occupancy Prediction in Dynamic Environments](https://mlsanity.com/paper/2011.09045/) (2020)
- [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)
- [Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks](https://mlsanity.com/paper/1909.11387/) (2019)

## Built on by (most cited first, 3 of 3)

- [Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps](https://mlsanity.com/paper/2308.04303/) (2023)
- [Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2407.15675/) (2024)
- [Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2602.07938/) (2026)

## Abstract (arXiv)

Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used egocentric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the traffic scene in a fixed frame, referred as allo-centric occupancy grid. This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'. We study the allo-centric grid prediction with different video prediction networks and validate the approach on the real-world Nuscenes dataset. The results demonstrate that the allo-centric grid representation significantly improves scene prediction, in comparison to the conventional ego-centric grid approach.