Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their generalization remain under-explored. While these questions can be studied by employing multiple datasets, it is challenging due to several discrepancies, \textit{e.g.,} in data formats, map resolution, and semantic annotation types. To address these challenges, we introduce UniTraj, a comprehensive framework that unifies various datasets, models, and evaluation criteria, presenting new opportunities for the vehicle trajectory prediction field. In particular, using UniTraj, we conduct extensive experiments and find that model performance significantly drops when transferred to other datasets. However, enlarging data size and diversity can substantially improve performance, leading to a new state-of-the-art result for the nuScenes dataset. We provide insights into dataset characteristics to explain these findings. The code can be found here: \hyperlink{https://github.com/vita-epfl/UniTraj}{https://github.com/vita-epfl/UniTraj}.

翻译：车辆轨迹预测日益依赖数据驱动方法，但模型在不同数据域的可扩展性以及更大数据集规模如何影响其泛化能力仍未被充分探索。尽管可以通过使用多个数据集来研究这些问题，但数据格式、地图分辨率、语义标注类型等差异带来了严峻挑战。为应对这些挑战，我们提出UniTraj——一个统一多样化数据集、模型与评估标准的综合框架，为车辆轨迹预测领域开辟了新机遇。特别是，利用UniTraj开展的大量实验表明，当模型迁移至其他数据集时性能显著下降。然而，增大数据规模与多样性可大幅提升性能，并在nuScenes数据集上实现了新的最优结果。我们通过剖析数据集特征为上述发现提供了深入解释。代码详见：\hyperlink{https://github.com/vita-epfl/UniTraj}{https://github.com/vita-epfl/UniTraj}。