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 problem, 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：预测周围物体的未来轨迹在机器人学与自动化领域（如自动驾驶）中具有关键作用。尽管基于学习的轨迹预测方法在公开基准测试中取得了显著性能，但此类方法的泛化能力仍存在疑问。在未见领域上的弱泛化能力——数据驱动方法的公认缺陷——可能损害轨迹预测模型在实际场景中的表现。因此，我们致力于提升模型的泛化能力，而非仅追求平均高精度。由于缺乏量化轨迹预测器泛化能力的基准测试，我们首先构建了一个名为argoverse-shift的新基准，其中各领域的数据分布存在显著差异。使用该基准进行评估时，我们发现领域偏移问题严重阻碍了轨迹预测器的泛化能力：当前最先进方法在面对分布外场景时性能大幅下降。为解决模型对领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。该策略利用Frenet坐标系进行建模，可有效缩小因道路几何与拓扑多样性导致的场景间领域差距。实验表明，我们的策略显著提升了最先进方法在模型未见领域上的预测性能，从而增强了数据驱动轨迹预测方法的泛化能力。