## 5 Conclusions  
An innovative MR-GCN-based framework is herein proposed to address frequent accidents and traffic flow issues caused by sudden lane changes in highway scenarios. The complex dynamics of vehicle lane changing in highway environments are accurately captured using continuous time-series images from a moving monocular camera, combined with a designed dynamic multi-level relational graph (DMLRG). The enhanced temporal interaction graph generation (ETIG) method further improved the model's accuracy in capturing dynamic vehicle interactions, especially under congested road conditions. The graph attention-based dynamic adjacency matrix update mechanism (GAMU) achieves dynamic adaptation to real-time traffic data by integrating an adaptive adjacency matrix update strategy with a multi-relational feature fusion technique.  
Experimental validation on multiple benchmark datasets, including KITTI, Apollo, and Indian datasets, showed that the proposed DMR-GCN framework significantly improves vehicle behavior prediction performance, with Map, Recall, and F1 scores of 90%, 88%, and 89%, respectively, outperforming existing algorithms. Additionally, tests on a custom high-speed traffic scene dataset further demonstrated the model's robustness and adaptability, achieving a Map of 91%, a Recall of 89%, and an F1 score of 90%. In summary, the DMR-GCN framework not only theoretically enhances traffic behavior modeling methodology but also shows strong application potential and stability in practice, providing robust support for efficient vehicle behavior prediction.  
Future work will optimize the model's real-time and generalization capabilities to adapt to more complex and dynamic traffic environments. Additionally, further research will explore integrating advanced machine learning techniques such as reinforcement learning to enhance decision-making processes in real-time traffic scenarios. Investigating the incorporation of multi-agent systems may also offer new insights into cooperative vehicle behaviors, ultimately contributing to safer and more efficient autonomous driving systems.  
Conceptualization, M.L. ([PERSON]) and W.Z.; methodology, M.L. ([PERSON]); software, M.L. ([PERSON]); validation, M.L. ([PERSON]), W.Z., M.L. ([PERSON]) and E.C.; formal analysis, M.L. ([PERSON]); investigation, M.L. ([PERSON]), E.C., C.H. and M.Z.; resources, M.L. ([PERSON]); data curation, M.L. ([PERSON]), C.H. and M.Z.; writing--original draft preparation, M.L. ([PERSON]); writing--review and editing, E.C.; supervision, W.G.; project administration, M.L. ([PERSON]); funding acquisition, W.Z. and W.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Shanghai Special Funds for Centralized Guided Local Science and Technology Development (YDZX0233100002002) and supported by the Postdoctoral Fellowship Program of CPSF under Grant Number GZB20240351.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in the study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Shanghai Smart Vehicle Cooperating Innovation Center Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.