  The trajectory prediction is a critical and challenging problem in the design
of an autonomous driving system. Many AI-oriented companies, such as Google
Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory
prediction algorithms. However, the prediction performance is governed by lots
of 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 its
surrounding vehicles. Meanwhile, the model encodes the historical trajectory
information of all the vehicles in the scene. Experiments are carried out on
the public NGSIM US-101 and I-80 Dataset and the prediction performance is
measured by the Root Mean Square Error (RMSE). The quantitative and qualitative
experimental results show that our model significantly improves the trajectory
prediction accuracy, by up to 50.00%, compared to existing models.
