## 1 Introduction
Although the abovementioned vehicle trajectory prediction methods based on RNN, CNN, and AM have achieved good prediction accuracy and promoted the development of the field of vehicle trajectory prediction to a certain extent, most of them extract the spatial interaction features among vehicles based on Euclidean data, while, in fact, the spatial interaction relations among vehicles are non-Euclidean [21]; this leads to the above methods having important limitations. As an efficient method to process non-Euclidean data, graph neural networks (GNNs) can avoid this problem by applying the algorithmic principle. The GRIP model [22] converts the raw vehicle data into graph data and then extracts the spatial-temporal interaction features in the graph data based on the convolution operation, further improving the accuracy of vehicle trajectory prediction. A further example is afforded by the GSTCN algorithm [21], which uses the reciprocal of the distance between two vehicles to construct the weighted adjacency matrix based on prior knowledge, and then learns the spatial-temporal interaction relations among vehicles based on graph convolutional networks (GCNs). However, GNN-based prediction methods still have the problem of inadequate spatial-temporal interaction modeling. Specifically, most of these methods only construct spatial graphs based on the raw vehicle position coordinate data, without making full use of other parameters of the vehicle. Moreover, they model temporal dependencies based on the extracted spatial interaction information which, to some extent, destroys the original temporal interaction structure and eventually leads to inadequate extraction of spatial-temporal interaction relations. To solve the above problems, this paper proposes a new trajectory prediction model called VTP-GCN. First, we comprehensively consider the historical coordinates and vehicle movement velocity information of the vehicles, propose the concept of spatial interaction coefficient (SIC) to characterize the intensity of spatial interaction between two vehicles, and optimize the weighted adjacency matrix of the existing spatial graph based on this coefficient. Meanwhile, to ensure the integrity of the temporal interaction structure entered into the temporal interaction extraction module, we construct a temporal graph based on the vehicle historical parameters rather than the extracted intra-spatial interaction features and utilize the self-attention mechanism to automatically capture and learn the interaction weights in the temporal weighted adjacency matrix. The interaction features hidden in the spatial and temporal graphs are extracted separately using GCN and then the two are fused to obtain the spatial-temporal interaction features of vehicles in the driving scene. In the trajectory prediction module, the spatial-temporal interaction features are used as input based on a fully convolutional operation to generate the trajectory positions of the vehicles in the next 5 s with only one forward step needed. The main contributions of our work are as follows. * A new vehicle trajectory prediction framework, VTP-GCN, is proposed to model the spatial-temporal interaction behavior and output the future motion trajectories of vehicles based on GCN and fully convolutional layers, with the data from the comparative experiments showing that this method is effective in improving the accuracy of vehicle trajectory prediction. * The concept of spatial interaction coefficient is proposed to mark the intensity of spatial interaction between two vehicles, with the results from the ablation test showing that the weighted adjacency matrix based on the spatial interaction coefficient can better model the spatial interaction behavior among vehicles and the corresponding model has a better prediction performance.