Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines
This addresses the need for interpretable models in autonomous driving, offering a glass-box alternative to black-box deep learning, though it is incremental as it applies an existing method to traffic data.
The study tackled traffic destination prediction by evaluating Explainable Boosting Machines (EBM) on datasets like SDD, InD, and Argoverse, achieving competitive performance for pedestrian destinations and modest results for vehicles while providing interpretability through feature analysis.
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.