  Forecasting the trajectories of neighbor vehicles is a crucial step for
decision making and motion planning of autonomous vehicles. This paper proposes
a graph-based spatial-temporal convolutional network (GSTCN) to predict future
trajectory distributions of all neighbor vehicles using past trajectories. This
network tackles the spatial interactions using a graph convolutional network
(GCN), and captures the temporal features with a convolutional neural network
(CNN). The spatial-temporal features are encoded and decoded by a gated
recurrent unit (GRU) network to generate future trajectory distributions.
Besides, we propose a weighted adjacency matrix to describe the intensities of
mutual influence between vehicles, and the ablation study demonstrates the
effectiveness of our proposed scheme. Our network is evaluated on two
real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation
Simulation (NGSIM).Comparisons in three aspects, including prediction errors,
model sizes, and inference speeds, show that our network can achieve
state-of-the-art performance.
