Trajectory prediction has garnered widespread attention in different fields, such as autonomous driving and robotic navigation. However, due to the significant variations in trajectory patterns across different scenarios, models trained in known environments often falter in unseen ones. To learn a generalized model that can directly handle unseen domains without requiring any model updating, we propose a novel meta-learning-based trajectory prediction method called MetaTra. This approach incorporates a Dual Trajectory Transformer (Dual-TT), which enables a thorough exploration of the individual intention and the interactions within group motion patterns in diverse scenarios. Building on this, we propose a meta-learning framework to simulate the generalization process between source and target domains. Furthermore, to enhance the stability of our prediction outcomes, we propose a Serial and Parallel Training (SPT) strategy along with a feature augmentation method named MetaMix. Experimental results on several real-world datasets confirm that MetaTra not only surpasses other state-of-the-art methods but also exhibits plug-and-play capabilities, particularly in the realm of domain generalization.

翻译：轨迹预测在自动驾驶、机器人导航等领域受到广泛关注。然而，由于不同场景下轨迹模式存在显著差异，在已知环境中训练的模型往往在未知场景中表现不佳。为学习一个无需模型更新就能直接处理未知域的泛化模型，我们提出了一种基于元学习方法的新型轨迹预测模型MetaTra。该方法包含一个双轨迹Transformer（Dual-TT），能够深入探索不同场景下的个体意图与群体运动模式中的交互关系。在此基础上，我们提出元学习框架来模拟源域与目标域之间的泛化过程。此外，为增强预测结果的稳定性，我们设计了串并行训练策略（SPT）与名为MetaMix的特征增强方法。在多个真实数据集上的实验结果表明，MetaTra不仅超越了其他现有最优方法，更在域泛化领域展现出即插即用的能力。