  The ability to model and predict ego-vehicle's surrounding traffic is crucial
for autonomous pilots and intelligent driver-assistance systems. Acceleration
prediction is important as one of the major components of traffic prediction.
This paper proposes novel approaches to the acceleration prediction problem. By
representing spatial relationships between vehicles with a graph model, we
build a generalized acceleration prediction framework. This paper studies the
effectiveness of proposed Graph Convolution Networks, which operate on graphs
predicting the acceleration distribution for vehicles driving on highways. We
further investigate prediction improvement through integrating of Recurrent
Neural Networks to disentangle the temporal complexity inherent in the traffic
data. Results from simulation studies using comprehensive performance metrics
support the conclusion that our proposed networks outperform state-of-the-art
methods in generating realistic trajectories over a prediction horizon.
