# SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model

arXiv: https://arxiv.org/abs/2509.17850 · HTML: https://mlsanity.com/paper/2509.17850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)
- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 23 of 23)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [PiP: Planning-informed Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2003.11476/) (2020)
- [Deep Imitative Models for Flexible Inference, Planning, and Control](https://mlsanity.com/paper/1810.06544/) (2018)
- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach](https://mlsanity.com/paper/2008.03880/) (2020)
- [Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization](https://mlsanity.com/paper/2110.13827/) (2021)
- [EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction](https://mlsanity.com/paper/2308.06564/) (2023)
- [Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation](https://mlsanity.com/paper/2207.05853/) (2022)
- [DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction](https://mlsanity.com/paper/2310.14570/) (2023)
- [Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation](https://mlsanity.com/paper/2408.00766/) (2024)
- [Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks](https://mlsanity.com/paper/2309.01981/) (2023)
- [Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2403.13674/) (2024)
- [Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning](https://mlsanity.com/paper/2303.03723/) (2023)
- [Bilevel Multi-Armed Bandit-Based Hierarchical Reinforcement Learning for Interaction-Aware Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2502.03960/) (2025)
- [Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction](https://mlsanity.com/paper/2207.11034/) (2022)
- [Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction](https://mlsanity.com/paper/2406.11941/) (2024)
- [Integrated Intention Prediction and Decision-Making with Spectrum Attention Net and Proximal Policy Optimization](https://mlsanity.com/paper/2408.03191/) (2024)

## Abstract (arXiv)

Accurate trajectory prediction of surrounding vehicles (SVs) is crucial for autonomous driving systems to avoid misguided decisions and potential accidents. However, achieving reliable predictions in highly dynamic and complex traffic scenarios remains a significant challenge. One of the key impediments lies in the limited effectiveness of current approaches to capture the multi-modal behaviors of drivers, which leads to predicted trajectories that deviate from actual future motions. To address this issue, we propose SocialTraj, a novel trajectory prediction framework integrating social psychology principles through social value orientation (SVO). By utilizing Bayesian inverse reinforcement learning (IRL) to estimate the SVO of SVs, we obtain the critical social context to infer the future interaction trend. To ensure modal consistency in predicted behaviors, the estimated SVOs of SVs are embedded into a conditional denoising diffusion model that aligns generated trajectories with historical driving styles. Additionally, the planned future trajectory of the ego vehicle (EV) is explicitly incorporated to enhance interaction modeling. Extensive experiments on NGSIM and HighD datasets demonstrate that SocialTraj is capable of adapting to highly dynamic and interactive scenarios while generating socially compliant and behaviorally consistent trajectory predictions, outperforming existing baselines. Ablation studies demonstrate that dynamic SVO estimation and explicit ego-planning components notably improve prediction accuracy and substantially reduce inference time.