  Pedestrian trajectory prediction is a crucial component in computer vision
and robotics, but remains challenging due to the domain shift problem. Previous
studies have tried to tackle this problem by leveraging a portion of the
trajectory data from the target domain to adapt the model. However, such domain
adaptation methods are impractical in real-world scenarios, as it is infeasible
to collect trajectory data from all potential target domains. In this paper, we
study a task named generalized pedestrian trajectory prediction, with the aim
of generalizing the model to unseen domains without accessing their
trajectories. To tackle this task, we introduce a Recurrent Aligned
Network~(RAN) to minimize the domain gap through domain alignment.
Specifically, we devise a recurrent alignment module to effectively align the
trajectory feature spaces at both time-state and time-sequence levels by the
recurrent alignment strategy.Furthermore, we introduce a pre-aligned
representation module to combine social interactions with the recurrent
alignment strategy, which aims to consider social interactions during the
alignment process instead of just target trajectories. We extensively evaluate
our method and compare it with state-of-the-art methods on three widely used
benchmarks. The experimental results demonstrate the superior generalization
capability of our method. Our work not only fills the gap in the generalization
setting for practical pedestrian trajectory prediction but also sets strong
baselines in this field.
