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

• URL: http://arxiv.org/abs/2602.13936v1
• Date: Sun, 15 Feb 2026 00:19:16 GMT
• Title: A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving
• Authors: Zhenyu Zong, Yuchen Wang, Haohong Lin, Lu Gan, Huajie Shao,
• Abstract summary: Trajectory prediction for traffic agents is critical for safe autonomous driving.<n>We aim to incorporate domain-invariant knowledge to enhance zero-shot trajectory prediction capabilities.<n>To address these challenges, we propose a novel Physics-guided Causal Model.<n>Experiments on real-world autonomous driving datasets demonstrate our method's superior zero-shot generalization performance in unseen cities.
• Score: 23.789035557791536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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