# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
## 5 Conclusions  
This paper presents the Multimodal Transformer Graph Convolutional Neural Network (MTGNet), a novel framework integrating Transformer and Graph Convolutional Network (GCN) to address multi-agent motion prediction in complex traffic scenarios by capturing long-term temporal dependencies and spatial interactions through a dynamic scene partitioning method based on panoramic fully connected agent traffic graphs, a vectorization strategy for reducing computational overhead, and a Transformer-based encoder for noise filtering. Taking agents' historical trajectories, scene maps, and auxiliary information as input, MTGNet achieves state-of-the-art performance on the Argoverse 2.0 dataset.  
The proposed framework demonstrates substantial practical value in autonomous driving systems, where it enables safe and efficient maneuver planning by accurately predicting surrounding agents' motions; in intelligent traffic management, where it optimizes traffic flow dynamics to alleviate congestion; and in advanced traffic surveillance, where it supports precise trajectory tracking for accident prevention and violation detection. Through its hybrid architecture combining Transformer's sequential modeling with GCN's spatial reasoning, dynamic scene adaptation, and noise-robust feature extraction, MTGNet not only outperforms existing baselines across key metrics but also establishes a robust baseline for future research. It offers generalizable solutions for integrating heterogeneous data (trajectories, scene geometry, agent attributes) in spatio-temporal prediction tasks, inspiring innovations in intelligent transportation systems and multi-agent motion analysis.  
[PERSON] conceived and designed the study, conducted a comprehensive literature review on multi-agent systems, developed the deep learning model, and drafted the initial manuscript. [PERSON] contributed to the study's conception, was responsible for the selection and analysis of relevant datasets, and participated in manuscript writing. [PERSON] handled data processing and optimization, as well as model evaluation and result analysis. [PERSON] reviewed and edited the manuscript, offering critical feedback on the methodology and interpretation of results. [PERSON] supervised the research and provided guidance on the development of the deep learning framework. [PERSON] managed the project and secured the necessary resources. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Industrial Key Core Technology Research Project of the Jilin Provincial Department of Science and Technology (Grant No. 20220201154 GX).  
Not applicable.  
Publicly available datasets were used in this study. These data can be found here: [[https://www.argoverse.org/av2.html#download-link](https://www.argoverse.org/av2.html#download-link)]([https://www.argoverse.org/av2.html#download-link](https://www.argoverse.org/av2.html#download-link)) (accessed on 30 August 2024).  
We would like to express our deepest gratitude to all those who have contributed to the completion of this research and the writing of this paper.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
MTGNet Multimodal Transformer Graph convolution neural network  
GCN Graph Convolutional Neural network  
CNN Convolutional Neural Network  
LSTM Long Short-Term Memory network  
RNN Recurrent Neural Network