While a lot of work has been done 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 study the performance of a state-of-the-art trajectory prediction method across four different datasets (Argoverse, NuScenes, Interaction, Shifts). We first check how a similar method can be applied and trained on all these datasets with similar hyperparameters. Then we highlight which datasets work best on others, and study how uncertainty estimation allows for a better transferable performance; proposing a novel way to estimate uncertainty and to directly use it in prediction.
翻译:虽然在制订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,我们研究了四个不同数据集(Argover、NuScenes、互动、 Shifts)的最新轨迹预测方法的性能。我们首先检查如何应用类似的方法,并培训使用所有具有类似超光谱的数据集。然后我们突出哪些数据集对其它数据集最有效,并研究不确定性估计如何有助于更好的可转移性能;提出一种新颖的方法来估计不确定性并将其直接用于预测。