# Personalized Trajectory Prediction via Distribution Discrimination

arXiv: https://arxiv.org/abs/2107.14204 · HTML: https://mlsanity.com/paper/2107.14204/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [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)
- [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-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks](https://mlsanity.com/paper/1907.03395/) (2019)
- [Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2005.08514/) (2020)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [Multiple Futures Prediction](https://mlsanity.com/paper/1911.00997/) (2019)
- [Variational Lossy Autoencoder](https://mlsanity.com/paper/1611.02731/) (2016)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Multi-Agent Tensor Fusion for Contextual Trajectory Prediction](https://mlsanity.com/paper/1904.04776/) (2019)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction](https://mlsanity.com/paper/1903.02793/) (2019)
- [TPNet: Trajectory Proposal Network for Motion Prediction](https://mlsanity.com/paper/2004.12255/) (2020)
- [Recursive Social Behavior Graph for Trajectory Prediction](https://mlsanity.com/paper/2004.10402/) (2020)
- [Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection](https://mlsanity.com/paper/1702.05552/) (2017)
- [Reciprocal Learning Networks for Human Trajectory Prediction](https://mlsanity.com/paper/2004.04340/) (2020)

## Built on by (most cited first, 30 of 30)

- [Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion](https://mlsanity.com/paper/2203.13777/) (2022)
- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction](https://mlsanity.com/paper/2207.09953/) (2022)
- [EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting](https://mlsanity.com/paper/2307.09306/) (2023)
- [SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model](https://mlsanity.com/paper/2403.18452/) (2024)
- [FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction](https://mlsanity.com/paper/2303.16574/) (2023)
- [Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction](https://mlsanity.com/paper/2403.18447/) (2024)
- [DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction](https://mlsanity.com/paper/2310.14570/) (2023)
- [Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction](https://mlsanity.com/paper/2304.04298/) (2023)
- [STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2211.11220/) (2022)
- [Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion](https://mlsanity.com/paper/2303.08367/) (2023)
- [Social Physics Informed Diffusion Model for Crowd Simulation](https://mlsanity.com/paper/2402.06680/) (2024)
- [Intention-aware Denoising Diffusion Model for Trajectory Prediction](https://mlsanity.com/paper/2403.09190/) (2024)
- [Vision-based Multi-future Trajectory Prediction: A Survey](https://mlsanity.com/paper/2302.10463/) (2023)
- [TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models](https://mlsanity.com/paper/2308.05985/) (2023)
- [ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2302.07583/) (2023)
- [SocialCircle+: Learning the Angle-based Conditioned Interaction Representation for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2409.14984/) (2024)
- [Continuous Locomotive Crowd Behavior Generation](https://mlsanity.com/paper/2504.04756/) (2025)
- [Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network](https://mlsanity.com/paper/2407.18551/) (2024)
- [Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations](https://mlsanity.com/paper/2412.02447/) (2024)
- [XVTP3D: Cross-view Trajectory Prediction Using Shared 3D Queries for Autonomous Driving](https://mlsanity.com/paper/2308.08764/) (2023)
- [Sparse Pedestrian Character Learning for Trajectory Prediction](https://mlsanity.com/paper/2311.15512/) (2023)
- [G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction](https://mlsanity.com/paper/2404.19330/) (2024)
- [Heterogeneous Interaction Modeling With Reduced Accumulated Error for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2410.21342/) (2024)
- [Rethinking Trajectory Prediction via "Team Game"](https://mlsanity.com/paper/2210.08793/) (2022)
- [Learning a Pedestrian Social Behavior Dictionary](https://mlsanity.com/paper/2212.01426/) (2022)
- [EANet: Expert Attention Network for Online Trajectory Prediction](https://mlsanity.com/paper/2309.05683/) (2023)
- [A Spatio-temporal Graph Network Allowing Incomplete Trajectory Input for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2501.13973/) (2025)
- [Schr"odinger's Cat: Probabilistic Representation and Prediction of Potential Scene Kinematics](https://mlsanity.com/paper/2607.25984/) (2026)
- [PRISM: Predictive Representation of Interaction Style and Motion for Social Robot Navigation](https://mlsanity.com/paper/2609.18125/) (2026)

## Abstract (arXiv)

Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics with both diversity and accuracy. In this paper, we present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions. Motivated by that the motion pattern of each person is personalized due to his/her habit, our DisDis learns the latent distribution to represent different motion patterns and optimize it by the contrastive discrimination. This distribution discrimination encourages latent distributions to be more discriminative. Our method can be integrated with existing multi-modal stochastic predictive models as a plug-and-play module to learn the more discriminative latent distribution. To evaluate the latent distribution, we further propose a new metric, probability cumulative minimum distance (PCMD) curve, which cumulatively calculates the minimum distance on the sorted probabilities. Experimental results on the ETH and UCY datasets show the effectiveness of our method.