# A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving

arXiv: https://arxiv.org/abs/2602.13936 · HTML: https://mlsanity.com/paper/2602.13936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 15 Feb 2026.

## 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)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction](https://mlsanity.com/paper/2104.00563/) (2021)
- [Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving](https://mlsanity.com/paper/2109.06446/) (2021)
- [Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders](https://mlsanity.com/paper/2308.09882/) (2023)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling](https://mlsanity.com/paper/2306.12241/) (2023)
- [Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment](https://mlsanity.com/paper/2309.13378/) (2023)
- [Human Trajectory Prediction via Counterfactual Analysis](https://mlsanity.com/paper/2107.14202/) (2021)
- [Causal Composition Diffusion Model for Closed-loop Traffic Generation](https://mlsanity.com/paper/2412.17920/) (2024)
- [CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships](https://mlsanity.com/paper/2207.03586/) (2022)
- [Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/2311.10747/) (2023)
- [Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments](https://mlsanity.com/paper/2309.13893/) (2023)
- [Improving the Generalizability of Trajectory Prediction Models with Frenet-Based Domain Normalization](https://mlsanity.com/paper/2305.17965/) (2023)
- [Physics Constrained Motion Prediction with Uncertainty Quantification](https://mlsanity.com/paper/2302.01060/) (2023)
- [torchode: A Parallel ODE Solver for PyTorch](https://mlsanity.com/paper/2210.12375/) (2022)
- [Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting](https://mlsanity.com/paper/2407.09475/) (2024)
- [Joint Perception and Prediction for Autonomous Driving: A Survey](https://mlsanity.com/paper/2412.14088/) (2024)
- [PBP: Path-based Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2309.03750/) (2023)
- [RMP: A Random Mask Pretrain Framework for Motion Prediction](https://mlsanity.com/paper/2309.08989/) (2023)
- [Goal-based Neural Physics Vehicle Trajectory Prediction Model](https://mlsanity.com/paper/2409.15182/) (2024)
- [Transfer Learning Study of Motion Transformer-based Trajectory Predictions](https://mlsanity.com/paper/2404.08271/) (2024)
- [Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations](https://mlsanity.com/paper/2312.04540/) (2023)
- [G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction](https://mlsanity.com/paper/2404.19330/) (2024)

## Built on by (most cited first, 1 of 1)

- [RankFormer: A Propose-then-Select Transformer for Multi-Agent Multimodal Trajectory Prediction](https://mlsanity.com/paper/2604.07126/) (2026)

## Abstract (arXiv)

Trajectory prediction for traffic agents is critical for safe autonomous driving. However, achieving effective zero-shot generalization in previously unseen domains remains a significant challenge. Motivated by the consistent nature of kinematics across diverse domains, we aim to incorporate domain-invariant knowledge to enhance zero-shot trajectory prediction capabilities. The key challenges include: 1) effectively extracting domain-invariant scene representations, and 2) integrating invariant features with kinematic models to enable generalized predictions. To address these challenges, we propose a novel generalizable Physics-guided Causal Model (PCM), which comprises two core components: a Disentangled Scene Encoder, which adopts intervention-based disentanglement to extract domain-invariant features from scenes, and a CausalODE Decoder, which employs a causal attention mechanism to effectively integrate kinematic models with meaningful contextual information. Extensive experiments on real-world autonomous driving datasets demonstrate our method's superior zero-shot generalization performance in unseen cities, significantly outperforming competitive baselines. The source code is released at https://github.com/ZY-Zong/Physics-guided-Causal-Model.