  Predicting the future trajectory of surrounding vehicles in a randomly
varying traffic level is one of the most challenging problems in developing an
autonomous vehicle. Since there is no pre-defined number of interacting
vehicles participate in, the prediction network has to be scalable with respect
to the vehicle number in order to guarantee the consistency in terms of both
accuracy and computational load. In this paper, the first fully scalable
trajectory prediction network, SCALE-Net, is proposed that can ensure both
higher prediction performance and consistent computational load regardless of
the number of surrounding vehicles. The SCALE-Net employs the Edge-enhance
Graph Convolutional Neural Network (EGCN) for the inter-vehicular interaction
embedding network. Since the proposed EGCN is inherently scalable with respect
to the graph node (an agent in this study), the model can be operated
independently from the total number of vehicles considered. We evaluated the
scalability of the SCALE-Net on the publically available NGSIM datasets by
comparing variations on computation time and prediction accuracy per single
driving scene with respect to the varying vehicle number. The experimental test
shows that both computation time and prediction performance of the SCALE-Net
consistently outperform those of previous models regardless of the level of
traffic complexities.
