  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.
