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[2602.13936] A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving
Summary
This paper presents a Physics-guided Causal Model for trajectory prediction in autonomous driving, focusing on zero-shot generalization across diverse domains.
Why It Matters
As autonomous driving technology advances, ensuring safety through accurate trajectory prediction is crucial. This research addresses the challenge of generalizing predictions in unseen environments, which is vital for real-world applications and improving the reliability of autonomous systems.
Key Takeaways
- Introduces a novel Physics-guided Causal Model for trajectory prediction.
- Focuses on achieving zero-shot generalization in unseen driving scenarios.
- Utilizes a Disentangled Scene Encoder and CausalODE Decoder for improved predictions.
- Demonstrates superior performance on real-world datasets compared to existing methods.
- Source code is available for further research and application.
Computer Science > Artificial Intelligence arXiv:2602.13936 (cs) [Submitted on 15 Feb 2026] Title:A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving Authors:Zhenyu Zong, Yuchen Wang, Haohong Lin, Lu Gan, Huajie Shao View a PDF of the paper titled A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving, by Zhenyu Zong and 3 other authors View PDF HTML (experimental) Abstract: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 d...
