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}.

UniTraj是一个统一各种数据集、模型和评估标准的综合框架，通过扩大数据规模和多样性可以显著提高车辆轨迹预测模型的性能，并且在nuScenes数据集上取得了最新的最佳结果。

UniTraj：基于可扩展车辆轨迹预测的统一框架