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 \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 …

autonomous autonomous driving boosting box capabilities challenge cs.lg debugging deep neural network driving fully autonomous glass interpretability machines nature network neural network prediction predictions solution through traffic trajectory transparency