Efficient and Interpretable Traffic Destination Prediction using
  Explainable Boosting Machines
Yasin Yousif Jörg Müller
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.