# Intention-Aware Diffusion Model for Pedestrian Trajectory Prediction

arXiv: https://arxiv.org/abs/2508.07146 · HTML: https://mlsanity.com/paper/2508.07146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion](https://mlsanity.com/paper/2304.01893/) (2023)
- [Leapfrog Diffusion Model for Stochastic Trajectory Prediction](https://mlsanity.com/paper/2303.10895/) (2023)
- [SocialVAE: Human Trajectory Prediction using Timewise Latents](https://mlsanity.com/paper/2203.08207/) (2022)
- [ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals](https://mlsanity.com/paper/2303.12071/) (2023)
- [Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis](https://mlsanity.com/paper/2103.07854/) (2021)
- [Interpretable Social Anchors for Human Trajectory Forecasting in Crowds](https://mlsanity.com/paper/2105.03136/) (2021)
- [EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting](https://mlsanity.com/paper/2307.09306/) (2023)
- [Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction](https://mlsanity.com/paper/2204.11561/) (2022)
- [SMEMO: Social Memory for Trajectory Forecasting](https://mlsanity.com/paper/2203.12446/) (2022)
- [DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction](https://mlsanity.com/paper/2310.14570/) (2023)
- [Progressive Pretext Task Learning for Human Trajectory Prediction](https://mlsanity.com/paper/2407.11588/) (2024)
- [Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights](https://mlsanity.com/paper/2502.20084/) (2025)
- [Path Planning in Dynamic Environments using Generative RNNs and Monte Carlo Tree Search](https://mlsanity.com/paper/2001.11597/) (2020)
- [C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction](https://mlsanity.com/paper/2412.13231/) (2024)
- [Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases](https://mlsanity.com/paper/2011.11190/) (2020)
- [Trajectory Prediction for Robot Navigation using Flow-Guided Markov Neural Operator](https://mlsanity.com/paper/2309.09137/) (2023)

## Built on by (most cited first, 2 of 2)

- [MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting](https://mlsanity.com/paper/2606.00471/) (2026)
- [GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction](https://mlsanity.com/paper/2609.13778/) (2026)

## Abstract (arXiv)

Predicting pedestrian motion trajectories is critical for the path planning and motion control of autonomous vehicles. Recent diffusion-based models have shown promising results in capturing the inherent stochasticity of pedestrian behavior for trajectory prediction. However, the absence of explicit semantic modelling of pedestrian intent in many diffusion-based methods may result in misinterpreted behaviors and reduced prediction accuracy. To address the above challenges, we propose a diffusion-based pedestrian trajectory prediction framework that incorporates both short-term and long-term motion intentions. Short-term intent is modelled using a residual polar representation, which decouples direction and magnitude to capture fine-grained local motion patterns. Long-term intent is estimated through a learnable, token-based endpoint predictor that generates multiple candidate goals with associated probabilities, enabling multimodal and context-aware intention modelling. Furthermore, we enhance the diffusion process by incorporating adaptive guidance and a residual noise predictor that dynamically refines denoising accuracy. The proposed framework is evaluated on the widely used ETH, UCY, and SDD benchmarks, demonstrating competitive results against state-of-the-art methods.