## 1 Introduction  
According to statistics, there are approximately 1.2 million high-speed traffic accidents yearly, resulting in numerous casualties. More than 30% of these accidents are caused by vehicle lane changing. The high incidence and severity of these accidents have prompted researchers to focus more on the study of vehicle behavior prediction. In recent years, with the rapid development of autonomous driving technology, the application of vehicle behavior prediction in high-speed scenarios has attracted considerable attention. Frequent vehicle lane changes lead to a decrease in overall traffic mobility and increase the likelihood of traffic accidents. Therefore, accurate vehicle behavior prediction can enhance the safety and efficiency of autonomous driving systems and lay a solid foundation for future intelligent transportation systems. In autonomous driving, the acquisition of environmental information through advanced sensing technologies such as LiDAR, radar, and cameras is essential for predicting vehicle behavior [1; 2]. Convolutional neural networks (CNNs) are widely employed to extract visual features for this purpose. However, reliance solely on visual data presents limitations, particularly in high-speed scenarios characterized by complex vehicle interactions [3; 4], often resulting in diminished prediction accuracy in dynamic environments [5]. To enhance prediction accuracy, researchers have investigated dynamic graph neural networks (DGNNs), which effectively model spatio-temporal dependencies by capturing dynamic interactions between vehicles [6; 7]. Approaches that combine graph convolutional networks (GCNs) with long short-term memory (LSTM) or temporal convolutional networks (TCNs) have shown promise in modeling these spatio-temporal dependencies [8; 9]. Despite these advancements, existing methods often struggle to accurately represent non-Euclidean spatial relationships between vehicles and to account for critical factors such as lane positioning and traffic flow in high-speed contexts [10; 11]. To address these challenges, researchers have proposed a method that integrates visual data with dynamic graph neural networks, employing CNNs to extract vehicle features and DMR-GCN to more effectively capture spatio-temporal dependencies [12; 13]. This approach constructs a traffic scene graph to represent vehicle layout and motion, thereby improving predictions in complex, dynamic environments [14; 15]. By effectively modeling non-Euclidean spatial relationships, the proposed method overcomes the limitations of existing techniques and enhances the accuracy of vehicle behavior predictions in high-speed scenarios [16; 17]. The integration of advanced graph neural networks with visual features opens new possibilities for precise and reliable behavior prediction, even under challenging conditions [18; 19]. Studies indicate that incorporating factors such as vehicle distance, lane position, and dynamic traffic flow significantly enhances prediction models [20; 21]. Future research may further explore hybrid methods that combine visual data with advanced neural network architectures to improve the robustness of vehicle behavior prediction [22]. The aim of the current study was to enhance the accuracy of vehicle behavior prediction in high-speed scenarios, addressing traffic accidents caused by lane changes. By integrating visual perception with a dynamic graph neural network (DMR-GCN) to construct a multilevel graph model that captures vehicle interactions, an adaptive network tuning mechanism and a dynamic scene learning strategy are herein introduced to improve prediction accuracy and safety. This approach addresses the shortcomings of existing methods in modeling complex interactions. It critically supports advancing autonomous driving and intelligent transportation systems, demonstrating significant practical value and broad application potential. The contributions made include the following:  
1.