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The ability to model and predict ego-vehicle's surrounding traffic is crucialfor autonomous pilots and intelligent driver-assistance systems. Accelerationprediction is important as one of the major components of traffic prediction.This paper proposes novel approaches to the acceleration prediction problem. Byrepresenting spatial relationships between vehicles with a graph model, webuild a generalized acceleration prediction framework. This paper studies theeffectiveness of proposed Graph Convolution Networks, which operate on graphspredicting the acceleration distribution for vehicles driving on highways. Wefurther investigate prediction improvement through integrating of RecurrentNeural Networks to disentangle the temporal complexity inherent in the trafficdata. Results from simulation studies using comprehensive performance metricssupport the conclusion that our proposed networks outperform state-of-the-artmethods in generating realistic trajectories over a prediction horizon.

Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration