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.

翻译：交通参与者的轨迹预测对于安全自动驾驶至关重要。然而，在先前未见过的领域中实现有效的零样本泛化仍然是一个重大挑战。受不同领域中运动学规律一致性本质的启发，我们旨在融入领域不变知识以增强零样本轨迹预测能力。关键挑战包括：1）有效提取领域不变的场景表征，以及2）将不变特征与运动学模型相结合以实现泛化预测。为解决这些挑战，我们提出了一种新颖的可泛化物理引导因果模型（PCM），该模型包含两个核心组件：一个解耦场景编码器，采用基于干预的解耦方法从场景中提取领域不变特征；以及一个CausalODE解码器，利用因果注意力机制将运动学模型与有意义的上下文信息有效整合。在真实世界自动驾驶数据集上的大量实验表明，我们的方法在未见过的城市中具有卓越的零样本泛化性能，显著优于竞争基线。源代码发布于 https://github.com/ZY-Zong/Physics-guided-Causal-Model。