# Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network

arXiv: https://arxiv.org/abs/2006.12906 · HTML: https://mlsanity.com/paper/2006.12906/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps](https://mlsanity.com/paper/2203.16910/) (2022)
- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation](https://mlsanity.com/paper/2110.09481/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure](https://mlsanity.com/paper/2105.02572/) (2021)
- [Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2202.05334/) (2022)
- [A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research](https://mlsanity.com/paper/2410.07066/) (2024)
- [Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2401.04872/) (2024)
- [Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks](https://mlsanity.com/paper/2308.06654/) (2023)
- [Intention Enhanced Diffusion Model for Multimodal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2508.04229/) (2025)
- [MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting](https://mlsanity.com/paper/2606.00471/) (2026)

## Abstract (arXiv)

Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between members of a crowd, including any response to a vehicle. Our approach, Probabilistic Crowd GAN, extends recent work in trajectory prediction, combining Recurrent Neural Networks (RNNs) with Mixture Density Networks (MDNs) to output probabilistic multimodal predictions, from which likely modal paths are found and used for adversarial training. We also propose the use of Graph Vehicle-Pedestrian Attention Network (GVAT), which models social interactions and allows input of a shared vehicle feature, showing that inclusion of this module leads to improved trajectory prediction both with and without the presence of a vehicle. Through evaluation on various datasets, we demonstrate improvements on the existing state of the art methods for trajectory prediction and illustrate how the true multimodal and uncertain nature of crowd interactions can be directly modelled.