  Pedestrian trajectory prediction is an essential component in a wide range of
AI applications such as autonomous driving and robotics. Existing methods
usually assume the training and testing motions follow the same pattern while
ignoring the potential distribution differences (e.g., shopping mall and
street). This issue results in inevitable performance decrease. To address this
issue, we propose a novel Transferable Graph Neural Network (T-GNN) framework,
which jointly conducts trajectory prediction as well as domain alignment in a
unified framework. Specifically, a domain-invariant GNN is proposed to explore
the structural motion knowledge where the domain-specific knowledge is reduced.
Moreover, an attention-based adaptive knowledge learning module is further
proposed to explore fine-grained individual-level feature representations for
knowledge transfer. By this way, disparities across different trajectory
domains will be better alleviated. More challenging while practical trajectory
prediction experiments are designed, and the experimental results verify the
superior performance of our proposed model. To the best of our knowledge, our
work is the pioneer which fills the gap in benchmarks and techniques for
practical pedestrian trajectory prediction across different domains.
