While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so far on the generalizability and the transferability of these methods across dataset. In this paper, we observe the performance of two of the latest state-of-the-art trajectory prediction methods across four different datasets (Argoverse, NuScenes, Interaction, Shifts). This analysis allows to gain some insights on the generalizability proprieties of most recent trajectory prediction models and to analyze which dataset is more representative of real driving scenes and therefore enables better transferability. Furthermore we present a novel method to estimate prediction uncertainty and show how it could be used to achieve better performance across datasets.
翻译:虽然在制定轨迹预测方法方面做了大量工作,并且为确定这项任务的基准提出了各种数据集,但迄今为止,关于这些方法在数据集之间的通用性和可转移性的研究很少。在本文件中,我们观察了四个不同数据集(Argoverse、NuScenes、互动、转移)的最新两种最先进的轨迹预测方法(Argover、NuScenes、Explocation、Explocation、Extrafts)的性能。这一分析使人们能够对最近的轨迹预测模型的通用性有一些了解,并分析哪些数据集更能代表真实的驱动场,因此更便于转移。此外,我们提出了一种新颖的方法来估计预测不确定性,并展示如何利用它实现更好的跨数据集的性能。