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.

翻译：预测周边物体未来轨迹在自动驾驶等机器人自动化场景中具有核心作用。尽管基于学习的轨迹预测方法在公开基准测试中取得了卓越性能，其泛化能力仍存疑问。数据驱动方法在未见域上的泛化能力薄弱这一公认缺陷，可能对轨迹预测模型的现实应用性能造成损害。因此，我们着力提升模型泛化能力而非单纯追求平均精度。由于缺乏量化轨迹预测模型泛化能力的基准，我们首先构建了名为argoverse-shift的新型基准，其各域的数据分布存在显著差异。利用该基准进行评估后，我们发现域偏移问题严重制约了轨迹预测器的泛化能力——现有最优方法在面对分布外场景时性能急剧下降。为增强模型应对域偏移问题的鲁棒性，我们提出了一种即插即用的轨迹预测域归一化策略。该策略采用Frenet坐标系进行建模，可有效缩小因道路几何拓扑差异造成的场景域间差距。实验表明，该策略显著提升了现有最优方法在模型未见域上的预测性能，从而改善了数据驱动轨迹预测方法的泛化能力。