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Integrating trajectory prediction to the decision-making and planning modulesof modular autonomous driving systems is expected to improve the safety andefficiency of self-driving vehicles. However, a vehicle's future trajectoryprediction is a challenging task since it is affected by the social interactivebehaviors of neighboring vehicles, and the number of neighboring vehicles canvary in different situations. This work proposes a GNN-RNN basedEncoder-Decoder network for interaction-aware trajectory prediction, wherevehicles' dynamics features are extracted from their historical tracks usingRNN, and the inter-vehicular interaction is represented by a directed graph andencoded using a GNN. The parallelism of GNN implies the proposed method'spotential to predict multi-vehicular trajectories simultaneously. Evaluation onthe dataset extracted from the NGSIM US-101 dataset shows that the proposedmodel is able to predict a target vehicle's trajectory in situations with avariable number of surrounding vehicles.

Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving