## 5 Conclusions  
In this paper, we propose a novel vehicle trajectory prediction scheme, VTP-GCN, in a connected vehicle environment. The model extracts spatial-temporal interaction features in the graph based on GCNs and performs trajectory prediction based on a fully convolutional operation in the temporal dimension. Based on the publicly available datasets I-80 and US-101, the following main conclusions are drawn:  
* The comparative experiment data show that, compared with the baseline models, the vehicle trajectory prediction model VTP-GCN based on graph convolutional neural networks proposed in this manuscript is associated with smaller prediction errors. Specifically, compared with GRIP, the average RMSE value of VTP-GCN is lower by 8%, effectively improving the prediction effectiveness of the future motion trajectories of all vehicles in the driving scene. * We model the spatial-temporal interactions among vehicles based on the SIC and the self-attention mechanism. The ablation experiment results of spatial interactions show that the prediction errors of the spatial interaction modeling method based on the traditional reciprocal Euclidean distance are larger, and its average RMSE value increases by 5% compared with the method based on the SIC. The results of the temporal interaction ablation experiment show that the trajectory prediction errors of the temporal interaction modeling method based on the reciprocal of the time interval are larger, and its average RMSE value increases by 12% compared with the method proposed in this study. * The ablation experiment data on the effectiveness of the feature extraction module show that the absence of any interaction module will lead to an increase in vehicle trajectory prediction errors. It is worth noting that the absence of SGCN causes  
Figure 9: Visualization of predicted trajectories. Green solid lines are the observed history, blue solid lines are the ground truth in the future, red dashed lines are the predicted trajectories (5 s) generated by our VTP-GCN. (**a**) Only one vehicle with temporal dependencies, (**b**) seven vehicles in a moderate traffic scene in which a vehicle will engage in lane changing, (**c**) thirteen vehicles in a heavy traffic scene with complex spatial–temporal dependencies. the model average prediction error to increase by 6%, while the absence of TGCN causes the model average prediction error to increase by 4%. Therefore, we speculate that spatial interaction modeling is of greater importance than temporal interaction modeling for vehicle trajectory prediction. In conclusion, the VTP-GCN vehicle trajectory prediction model proposed in this study can model the spatial-temporal interaction behavior among vehicles based on 3 s of historical motion state data, and effectively predict the motion trajectories of all vehicles within the set scene range in the following 5 s. However, the model proposed in this study operates under ideal conditions, that is, we assume that the historical movement data of all vehicles in the scene can be accurately obtained, and does not consider the occlusion, noise, or uncertainty that exist in the networked environment. Therefore, in the subsequent work, we strive to address the issue. At the same time, we will evaluate our prediction model using other datasets, e.g., the HighD dataset or Interaction dataset, in which driving scenes are richer. In addition, we will further enrich the research based on this paper, so that the prediction model can effectively generate the future motion trajectories of all participants in the scenes, including pedestrians and two-wheelers. [PERSON], [PERSON] and [PERSON] contributed equally to the formulation of the idea, the design of experiments, the analysis and interpretation of results, and the writing and improvement of the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 52202503.