  Predicting the future trajectories of nearby objects plays a pivotal role in
Robotics and Automation such as autonomous driving. While learning-based
trajectory prediction methods have achieved remarkable performance on public
benchmarks, the generalization ability of these approaches remains
questionable. The poor generalizability on unseen domains, a well-recognized
defect of data-driven approaches, can potentially harm the real-world
performance of trajectory prediction models. We are thus motivated to improve
generalization ability of models instead of merely pursuing high accuracy on
average. Due to the lack of benchmarks for quantifying the generalization
ability of trajectory predictors, we first construct a new benchmark called
argoverse-shift, where the data distributions of domains are significantly
different. Using this benchmark for evaluation, we identify that the domain
shift problem seriously hinders the generalization of trajectory predictors
since state-of-the-art approaches suffer from severe performance degradation
when facing those out-of-distribution scenes. To enhance the robustness of
models against domain shift problems, we propose a plug-and-play strategy for
domain normalization in trajectory prediction. Our strategy utilizes the Frenet
coordinate frame for modeling and can effectively narrow the domain gap of
different scenes caused by the variety of road geometry and topology.
Experiments show that our strategy noticeably boosts the prediction performance
of the state-of-the-art in domains that were previously unseen to the models,
thereby improving the generalization ability of data-driven trajectory
prediction methods.
