# TrajPRed: Trajectory Prediction with Region-based Relation Learning

arXiv: https://arxiv.org/abs/2404.06971 · HTML: https://mlsanity.com/paper/2404.06971/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 67 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 25 of 25)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [An Exponential Learning Rate Schedule for Deep Learning](https://mlsanity.com/paper/1910.07454/) (2019)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [Conditional Generative Neural System for Probabilistic Trajectory Prediction](https://mlsanity.com/paper/1905.01631/) (2019)
- [The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction](https://mlsanity.com/paper/1912.06445/) (2019)
- [Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking](https://mlsanity.com/paper/2102.09117/) (2021)
- [Context-Aware Trajectory Prediction](https://mlsanity.com/paper/1705.02503/) (2017)
- [Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction](https://mlsanity.com/paper/1907.10178/) (2019)
- [Scene-LSTM: A Model for Human Trajectory Prediction](https://mlsanity.com/paper/1808.04018/) (2018)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces](https://mlsanity.com/paper/2202.08506/) (2022)
- [Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation](https://mlsanity.com/paper/2206.08158/) (2022)
- [Explanatory Paradigms in Neural Networks](https://mlsanity.com/paper/2202.11838/) (2022)
- [Dynamic Channel: A Planning Framework for Crowd Navigation](https://mlsanity.com/paper/1903.00143/) (2019)
- [Patient Aware Active Learning for Fine-Grained OCT Classification](https://mlsanity.com/paper/2206.11485/) (2022)
- [Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence Tomography Classification](https://mlsanity.com/paper/2203.10622/) (2022)
- [Open-Set Recognition with Gradient-Based Representations](https://mlsanity.com/paper/2206.08229/) (2022)
- [AutoTrajectory: Label-free Trajectory Extraction and Prediction from Videos using Dynamic Points](https://mlsanity.com/paper/2007.05719/) (2020)
- [A Gating Model for Bias Calibration in Generalized Zero-shot Learning](https://mlsanity.com/paper/2203.04195/) (2022)
- [On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption](https://mlsanity.com/paper/2009.00817/) (2020)
- [Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation](https://mlsanity.com/paper/2302.14644/) (2023)

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

- [Social-Pose: Enhancing Trajectory Prediction with Human Body Pose](https://mlsanity.com/paper/2507.22742/) (2025)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [Encore: Conditioning Trajectory Forecasting via Biased Ego Rehearsals](https://mlsanity.com/paper/2605.11463/) (2026)

## Abstract (arXiv)

Forecasting human trajectories in traffic scenes is critical for safety within mixed or fully autonomous systems. Human future trajectories are driven by two major stimuli, social interactions, and stochastic goals. Thus, reliable forecasting needs to capture these two stimuli. Edge-based relation modeling represents social interactions using pairwise correlations from precise individual states. Nevertheless, edge-based relations can be vulnerable under perturbations. To alleviate these issues, we propose a region-based relation learning paradigm that models social interactions via region-wise dynamics of joint states, i.e., the changes in the density of crowds. In particular, region-wise agent joint information is encoded within convolutional feature grids. Social relations are modeled by relating the temporal changes of local joint information from a global perspective. We show that region-based relations are less susceptible to perturbations. In order to account for the stochastic individual goals, we exploit a conditional variational autoencoder to realize multi-goal estimation and diverse future prediction. Specifically, we perform variational inference via the latent distribution, which is conditioned on the correlation between input states and associated target goals. Sampling from the latent distribution enables the framework to reliably capture the stochastic behavior in test data. We integrate multi-goal estimation and region-based relation learning to model the two stimuli, social interactions, and stochastic goals, in a prediction framework. We evaluate our framework on the ETH-UCY dataset and Stanford Drone Dataset (SDD). We show that the diverse prediction better fits the ground truth when incorporating the relation module. Our framework outperforms the state-of-the-art models on SDD by 27.61%/18.20% of ADE/FDE metrics.