Traffic Forecasting using Vehicle-to-Vehicle Communication

Vehicle-to-vehicle (V2V) communication is utilized in order to provide real-time on-board traffic predictions. A hybrid approach is proposed where physics based models are supplemented with deep learning. A recurrent neural network is used to improve the accuracy of predictions given by first principle models. Our hybrid model is able to predict the velocity of individual vehicles up to 40 seconds into the future with improved accuracy over physics based baselines. A comprehensive study is conducted to evaluate different methods of integrating physics with deep learning.