Abstract:We take the first step in using vehicle-to-vehicle (V2V) communication to provide real-time onboard traffic predictions. In order to best utilize real-world V2V communication data, we integrate first principle models with deep learning. Specifically, we train recurrent neural networks to improve the predictions given by first principle models. Our approach is able to predict the velocity of individual vehicles up to a minute into the future with improved accuracy over first principlebased baselines. We conduct… Show more
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