# Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning

arXiv: https://arxiv.org/abs/1904.12374 · HTML: https://mlsanity.com/paper/1904.12374/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2019.

## Built on by (most cited first, 18 of 18)

- [Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction](https://mlsanity.com/paper/2211.08701/) (2022)
- [How Do We Fail? Stress Testing Perception in Autonomous Vehicles](https://mlsanity.com/paper/2203.14155/) (2022)
- [Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments](https://mlsanity.com/paper/2209.13172/) (2022)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Multi-Agent Variational Occlusion Inference Using People as Sensors](https://mlsanity.com/paper/2109.02173/) (2021)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [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)
- [Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving](https://mlsanity.com/paper/2407.21126/) (2024)
- [Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning](https://mlsanity.com/paper/2103.14718/) (2021)
- [LOPR: Latent Occupancy PRediction using Generative Models](https://mlsanity.com/paper/2210.01249/) (2022)
- [SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving](https://mlsanity.com/paper/2102.09883/) (2021)
- [Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks](https://mlsanity.com/paper/2301.04454/) (2023)
- [A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field](https://mlsanity.com/paper/2309.10948/) (2023)
- [Deep Occupancy-Predictive Representations for Autonomous Driving](https://mlsanity.com/paper/2303.04218/) (2023)
- [3D Radar Velocity Maps for Uncertain Dynamic Environments](https://mlsanity.com/paper/2107.11039/) (2021)
- [Does Unpredictability Influence Driving Behavior?](https://mlsanity.com/paper/2307.15287/) (2023)
- [Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2602.07938/) (2026)

## Abstract (arXiv)

A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles. A reliable prediction of the future environment, including the behavior of dynamic agents, would allow planning algorithms to proactively generate a trajectory in response to a rapidly changing environment. We present a novel framework that predicts the future occupancy state of the local environment surrounding an autonomous agent by learning a motion model from occupancy grid data using a neural network. We take advantage of the temporal structure of the grid data by utilizing a convolutional long-short term memory network in the form of the PredNet architecture. This method is validated on the KITTI dataset and demonstrates higher accuracy and better predictive power than baseline methods.