## 6 Conclusions  
In this paper, an attention-based vehicle trajectory prediction architecture considering multi-vehicle interactions has been proposed. Firstly, the motion interaction between the target vehicle and surrounding vehicles is described as the directed graph and the GAT is adopted to extract the interaction features. Unlike the tradition method, which calculates the edge attention weight by directly concatenating the node features, we introduce the repulsive force between vehicles as a priori edge information to better represent the potential collision risk. Then, the vehicle-vehicle interaction features are integrated into the embedded trajectory vectors and the spatio-temporal attention mechanism is applied to excavate the significance of hidden features, guiding the decoder to generate a plausible future trajectory. The NGSIM public dataset was chosen for training and evaluation of the proposed prediction model. The test results reveal that compared with the existing prediction models, our method can reduce the prediction error and maintain good robustness both for short and long prediction horizons. It is also demonstrated that even in a dense traffic environment, the proposed model can accurately predict the future trajectory of the objecti vehicle even when there are lane-keeping and lane-changing maneuvers, meaning it provides reliable information for decision-making systems of autonomous vehicles.  
In future work, we will expand the network by exploring richer interaction features, that is, not only vehicle-vehicle interaction but also vehicle-pedestrian interaction in dynamic driving environments, enabling better scenario adaptation and robustness of the prediction model. In addition, map information has not yet been utilized for the prediction model. With the gradual expansion of high-precision maps, we will incorporate map data into the model to further improve its prediction accuracy.  
Conceptualization, J.Z. and Y.R.; methodology, Y.R.; software, Y.R. and J.L.; validation, J.Z. and Y.R.; formal analysis, K.W.; investigation, X.H.; resources, K.W.; data curation, X.H.; writing--original draft preparation, J.Z.; writing--review and editing, Y.R.; visualization, J.L.; supervision, Y.R.; project administration, K.W.; funding acquisition, J.Z. and K.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Innovation Key R&D Program of Chongqing, no. cstc2021 jscc-cylhX0006; a National Key Research and Development Program of China Grant, no. 2022 YFF0604900; and the Open Fund of the Chongqing Key Laboratory of Industry and Information of Automotive Active Safety Testing Technology, no. 22 AKC03.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the commercial privacy.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company China Merchants Testing Vehicle Technology Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relation-ships that could be construed as a potential conflict of interest.