## 6 Conclusions  
Predicting the trajectory of surrounding traffic is one of the essential capabilities of autonomous vehicles [44]. The vehicle senses the historical trajectories of moving objects and predicts their future behavior intentions. Taking the predicted trajectories of moving objects such as obstacles, pedestrians, and non-motorized vehicles as input, autonomous vehicles can make more reasonable driving decisions and plan more reasonable and safe vehicle movement behaviors. In order to predict vehicle trajectories more accurately, it is necessary to provide more accurate and feature-rich vehicle trajectory data and better vehicle trajectory prediction methods. In this paper, for a more accurate prediction of vehicle trajectory, more accurate and feature-rich vehicle trajectory data with a better vehicle trajectory prediction method is given. The main findings are outlined below.  
* The prediction method considers the interaction between traffic agents and the influence of traffic rules on vehicle trajectories, such as the signal phase. This research proposes a novel framework, called TPASTGCN, which fully utilizes multilevel features for vehicle trajectory anticipation tasks. The effectiveness of our proposal is validated by a real-world dataset and several mainstream methods, which demonstrates the excellent performance in generating future trajectories at the highly complicated intersection scenes. The results manifest that compared with the baseline models, the proposed scheme achieves the prediction accuracy improvement with over 23% on average and 61% at most.
* Additionally, the experiment also indicates that the model is able to address the intention prediction issue to some extent, and the model and its effectiveness are superior to existing trajectory prediction methods in intersection scenarios. But the trajectory prediction model accuracy has further room for improvement. A vehicle's future trajectory is under the influence of more factors than we are able to consider here, such as vehicle interactions, driver intentions, traffic rules, etc. In this paper, only the factors of vehicle interaction and signal phasing are considered, but the remaining time of traffic signals may improve the accuracy of prediction. Thus, we encourage the authors of future research to model more influencing factors within the the model.
* The accuracy and real-time requirements for the trajectory are very high. The proposed model achieves an equilibrium of prediction speed and accuracy, but its effectiveness on real vehicles is not verified. In the future, we need to conduct extensive testing, validation, and additional research at the vehicle end to verify the validity of our models, and we intend to build spatial-temporal navigation maps and make path-planning tasks for autonomous vehicles based on this study to improve the safety and comfort of autonomous driving.  
Conceptualization, H.L. and Y.R.; Methodology, W.C.; Software, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2021 YFB1600504).  
Figure 11: Explanations of the model’s working mechanisms.