Developing accurate models for traffic trajectory predictions is crucial for
achieving fully autonomous driving. Various deep neural network models have
been employed to address this challenge, but their black-box nature hinders
transparency and debugging capabilities in a deployed system. Glass-box models
offer a solution by providing full interpretability through methods like
\ac{GAM}. In this study, we evaluate an efficient additive model called
\ac{EBM} for traffic prediction on three popular mixed traffic datasets:
\ac{SDD}, \ac{InD}, and Argoverse. Our results show that the \ac{EBM} models
perform competitively in predicting pedestrian destinations within \ac{SDD} and
\ac{InD} while providing modest predictions for vehicle-dominant Argoverse
dataset. Additionally, our transparent trained models allow us to analyse
feature importance and interactions, as well as provide qualitative examples of
predictions explanation. The full training code will be made public upon
publication.

通过评估一种高效的可加性模型，本研究在三个热门混合交通数据集上对交通预测进行了评估，结果显示该模型在预测行人目的地方面表现竞争力，对以车辆为主的数据集则提供较为适度的预测，而透明的训练模型使我们能够分析特征的重要性和相互作用，并提供预测解释的质量性示例。