Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines

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 Generalized Additive Models (GAM). In this study, we evaluate an efficient additive model called Explainable Boosting Machines (EBM) for traffic prediction on three popular mixed traffic datasets: Stanford drone dataset (SDD), Intersection Drone Dataset (InD), and Argoverse. Our results show that the EBM models perform competitively in predicting pedestrian destinations within SDD and 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.