# Improving Transferability for Cross-domain Trajectory Prediction via Neural Stochastic Differential Equation

arXiv: https://arxiv.org/abs/2312.15906 · HTML: https://mlsanity.com/paper/2312.15906/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [Neural Controlled Differential Equations for Irregular Time Series](https://mlsanity.com/paper/2005.08926/) (2020)
- [Scalable Gradients for Stochastic Differential Equations](https://mlsanity.com/paper/2001.01328/) (2020)
- [GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series](https://mlsanity.com/paper/1905.12374/) (2019)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit](https://mlsanity.com/paper/1905.09883/) (2019)
- [Human Trajectory Forecasting in Crowds: A Deep Learning Perspective](https://mlsanity.com/paper/2007.03639/) (2020)
- [Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise](https://mlsanity.com/paper/1906.02355/) (2019)
- [Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks](https://mlsanity.com/paper/2107.07455/) (2021)
- [SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates](https://mlsanity.com/paper/2008.10546/) (2020)
- [MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction](https://mlsanity.com/paper/2201.07189/) (2022)
- [Neural ODE Processes](https://mlsanity.com/paper/2103.12413/) (2021)
- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2305.14715/) (2023)
- [Non-Probability Sampling Network for Stochastic Human Trajectory Prediction](https://mlsanity.com/paper/2203.13471/) (2022)
- [Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation](https://mlsanity.com/paper/2010.08188/) (2020)
- [Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking](https://mlsanity.com/paper/2012.02337/) (2020)
- [Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision](https://mlsanity.com/paper/2012.01884/) (2020)
- [Transferable and Adaptable Driving Behavior Prediction](https://mlsanity.com/paper/2202.05140/) (2022)
- [MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs](https://mlsanity.com/paper/2302.00735/) (2023)
- [Collaborative Uncertainty in Multi-Agent Trajectory Forecasting](https://mlsanity.com/paper/2110.13947/) (2021)
- [Propagating State Uncertainty Through Trajectory Forecasting](https://mlsanity.com/paper/2110.03267/) (2021)
- [Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders](https://mlsanity.com/paper/2303.02320/) (2023)
- [Forecasting Human Trajectory from Scene History](https://mlsanity.com/paper/2210.08732/) (2022)
- [Uncertainty estimation for Cross-dataset performance in Trajectory prediction](https://mlsanity.com/paper/2205.07310/) (2022)
- [Improving the Generalizability of Trajectory Prediction Models with Frenet-Based Domain Normalization](https://mlsanity.com/paper/2305.17965/) (2023)

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

- [Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning](https://mlsanity.com/paper/2404.05218/) (2024)
- [Adapting to Length Shift: FlexiLength Network for Trajectory Prediction](https://mlsanity.com/paper/2404.00742/) (2024)
- [T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory](https://mlsanity.com/paper/2403.10052/) (2024)
- [Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports](https://mlsanity.com/paper/2405.17680/) (2024)
- [Continuous Locomotive Crowd Behavior Generation](https://mlsanity.com/paper/2504.04756/) (2025)
- [Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model](https://mlsanity.com/paper/2507.22615/) (2025)
- [Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning](https://mlsanity.com/paper/2507.04790/) (2025)
- [Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning](https://mlsanity.com/paper/2507.12977/) (2025)
- [AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports](https://mlsanity.com/paper/2509.16095/) (2025)
- [MetaDAT: Generalizable Trajectory Prediction via Meta Pre-training and Data-Adaptive Test-Time Updating](https://mlsanity.com/paper/2603.09419/) (2026)
- [STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation](https://mlsanity.com/paper/2604.02756/) (2026)
- [Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings](https://mlsanity.com/paper/2606.30777/) (2026)

## Abstract (arXiv)

Multi-agent trajectory prediction is crucial for various practical applications, spurring the construction of many large-scale trajectory datasets, including vehicles and pedestrians. However, discrepancies exist among datasets due to external factors and data acquisition strategies. External factors include geographical differences and driving styles, while data acquisition strategies include data acquisition rate, history/prediction length, and detector/tracker error. Consequently, the proficient performance of models trained on large-scale datasets has limited transferability on other small-size datasets, bounding the utilization of existing large-scale datasets. To address this limitation, we propose a method based on continuous and stochastic representations of Neural Stochastic Differential Equations (NSDE) for alleviating discrepancies due to data acquisition strategy. We utilize the benefits of continuous representation for handling arbitrary time steps and the use of stochastic representation for handling detector/tracker errors. Additionally, we propose a dataset-specific diffusion network and its training framework to handle dataset-specific detection/tracking errors. The effectiveness of our method is validated against state-of-the-art trajectory prediction models on the popular benchmark datasets: nuScenes, Argoverse, Lyft, INTERACTION, and Waymo Open Motion Dataset (WOMD). Improvement in performance gain on various source and target dataset configurations shows the generalized competence of our approach in addressing cross-dataset discrepancies.