Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines

• URL: http://arxiv.org/abs/2402.03457v1
• Date: Mon, 5 Feb 2024 19:09:42 GMT
• Title: Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines
• Authors: Yasin Yousif and J\"org M\"uller
• Abstract summary: We evaluate an efficient additive model called acEBM for traffic prediction on three popular mixed traffic datasets. Our results show that the acEBM models perform competitively in predicting pedestrian destinations within acSDD and acInD while providing modest predictions for vehicle-dominant Argoverse dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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