  We take the first step in using vehicle-to-vehicle (V2V) communication to
provide real-time on-board 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 principle-based baselines. We conduct a
comprehensive study to evaluate different methods of integrating first
principle models with deep learning techniques. The source code for our models
is available at https://github.com/Rose-STL-Lab/V2V-traffic-forecast .
