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Predicting the future trajectory of surrounding vehicles in a randomlyvarying traffic level is one of the most challenging problems in developing anautonomous vehicle. Since there is no pre-defined number of interactingvehicles participate in, the prediction network has to be scalable with respectto the vehicle number in order to guarantee the consistency in terms of bothaccuracy and computational load. In this paper, the first fully scalabletrajectory prediction network, SCALE-Net, is proposed that can ensure bothhigher prediction performance and consistent computational load regardless ofthe number of surrounding vehicles. The SCALE-Net employs the Edge-enhanceGraph Convolutional Neural Network (EGCN) for the inter-vehicular interactionembedding network. Since the proposed EGCN is inherently scalable with respectto the graph node (an agent in this study), the model can be operatedindependently from the total number of vehicles considered. We evaluated thescalability of the SCALE-Net on the publically available NGSIM datasets bycomparing variations on computation time and prediction accuracy per singledriving scene with respect to the varying vehicle number. The experimental testshows that both computation time and prediction performance of the SCALE-Netconsistently outperform those of previous models regardless of the level oftraffic complexities.

SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random  Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural  Network