# AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention

arXiv: https://arxiv.org/abs/2101.05682 · HTML: https://mlsanity.com/paper/2101.05682/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 14 Jan 2021.

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

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks](https://mlsanity.com/paper/1907.03395/) (2019)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction](https://mlsanity.com/paper/1903.02793/) (2019)
- [Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction](https://mlsanity.com/paper/1906.03631/) (2019)
- [Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs](https://mlsanity.com/paper/1904.09507/) (2019)
- [Collaborative Motion Prediction via Neural Motion Message Passing](https://mlsanity.com/paper/2003.06594/) (2020)
- [Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings](https://mlsanity.com/paper/1806.02813/) (2018)
- [Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze](https://mlsanity.com/paper/1909.10400/) (2019)
- [Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy](https://mlsanity.com/paper/1906.01529/) (2019)
- [Gaze Training by Modulated Dropout Improves Imitation Learning](https://mlsanity.com/paper/1904.08377/) (2019)
- [CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation](https://mlsanity.com/paper/2005.00754/) (2020)

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

- [Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders](https://mlsanity.com/paper/2308.09882/) (2023)
- [SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.05370/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2504.15616/) (2025)
- [Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations](https://mlsanity.com/paper/2412.02447/) (2024)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and Cognitive Theory](https://mlsanity.com/paper/2311.08086/) (2023)
- [The Social Context of Human-Robot Interactions](https://mlsanity.com/paper/2508.13982/) (2025)
- [Fighter flight trajectory prediction based on spatio-temporal graphcial attention network](https://mlsanity.com/paper/2405.08034/) (2024)
- [Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning](https://mlsanity.com/paper/2602.22791/) (2026)
- [SceneSelect: Selective Learning for Trajectory Scene Classification and Expert Scheduling](https://mlsanity.com/paper/2604.24514/) (2026)
- [Encore: Conditioning Trajectory Forecasting via Biased Ego Rehearsals](https://mlsanity.com/paper/2605.11463/) (2026)

## Abstract (arXiv)

Pedestrian trajectory prediction is a critical yet challenging task, especially for crowded scenes. We suggest that introducing an attention mechanism to infer the importance of different neighbors is critical for accurate trajectory prediction in scenes with varying crowd size. In this work, we propose a novel method, AVGCN, for trajectory prediction utilizing graph convolutional networks (GCN) based on human attention (A denotes attention, V denotes visual field constraints). First, we train an attention network that estimates the importance of neighboring pedestrians, using gaze data collected as subjects perform a bird's eye view crowd navigation task. Then, we incorporate the learned attention weights modulated by constraints on the pedestrian's visual field into a trajectory prediction network that uses a GCN to aggregate information from neighbors efficiently. AVGCN also considers the stochastic nature of pedestrian trajectories by taking advantage of variational trajectory prediction. Our approach achieves state-of-the-art performance on several trajectory prediction benchmarks, and the lowest average prediction error over all considered benchmarks.