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The trajectory prediction is a critical and challenging problem in the designof an autonomous driving system. Many AI-oriented companies, such as GoogleWaymo, Uber and DiDi, are investigating more accurate vehicle trajectoryprediction algorithms. However, the prediction performance is governed by lotsof entangled factors, such as the stochastic behaviors of surrounding vehicles,historical information of self-trajectory, and relative positions of neighbors,etc. In this paper, we propose a novel graph-based information sharing network(GISNet) that allows the information sharing between the target vehicle and itssurrounding vehicles. Meanwhile, the model encodes the historical trajectoryinformation of all the vehicles in the scene. Experiments are carried out onthe public NGSIM US-101 and I-80 Dataset and the prediction performance ismeasured by the Root Mean Square Error (RMSE). The quantitative and qualitativeexperimental results show that our model significantly improves the trajectoryprediction accuracy, by up to 50.00%, compared to existing models.

GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction