## 4 Experimentation and Analysis
### Qualitative Results
#### 4.2.2 Comparison Experiments  
To validate the proposed method, it was trained and tested on several datasets, including the Apollo, KITTI, and Indian databases. Comparisons were made with the baseline model, the LSTM-based graph network Structural-RNN, and the LSTM + Multi-head Attention model. The experimental results are shown in Table 2 and Figure 9. These comparison experiments indicate that the proposed method outperforms other models across the various datasets.  
Specifically, for the continuous left- and right-lane change (CLAL and CLAR) metrics, our method improved from 0.80 and 0.79 to 0.88 and 0.87 on the Apollo dataset. Similarly, it improved from 0.81 and 0.80 to 0.89 and 0.88 on the KITTI dataset and from 0.82 and 0.81 to 0.90 and 0.89 on the Indian dataset. These improvements highlight the advantages of dynamic multilayer relational maps in effectively capturing complex interactions between vehicles and road objects.  
For the left- and right-lane change (LCL and LCR) metrics, the MR-GCN framework showed enhancements from 0.77 and 0.76 to 0.86 and 0.85 on the Apollo dataset. It also improved from 0.78 and 0.77 to 0.87 and 0.86 on the KITTI dataset and from 0.79 and 0.78 to 0.88 and 0.87 on the Indian dataset, demonstrating higher accuracy and robustness. This illustrates the efficacy of the MR-GCN framework in addressing diverse datasets and intricate interaction behaviors.  
In terms of overtaking (OVT) metrics, improvements were observed on the Apollo dataset, increasing from 0.74 to 0.83. Similarly, the KITTI dataset increased from 0.75 to 0.84, while the Indian dataset improved from 0.76 to 0.85. These results underscore the method's effectiveness in complex traffic scenarios, confirming the MR-GCN framework's ability to handle real-time dynamic data.  
Additionally, the MR-GCN framework exhibited excellent performance in average inference time and processing speed, achieving an average inference time of 35 milliseconds per frame and a processing speed of 28.6 frames per second, a significant improvement over other models. This short inference time and high frame rate enable it to respond to input data rapidly, making it well suited for real-time applications such as autonomous driving and traffic management systems. Reacting quickly and accurately is crucial for reducing accidents and enhancing road safety. Consequently, the MR-GCN framework's outstanding performance in dynamic traffic environments indicates its broad application potential in intelligent transportation systems. Furthermore, future optimizations of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Train and Test On**} & \multicolumn{3}{c}{**Agolla**} & \multicolumn{3}{c}{**KITTI**} & \multicolumn{3}{c}{**Indian**} \\ \cline{3-13} \multicolumn{1}{c}{**Action**} & \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**St-RNN**} & \multicolumn{3}{c}{**LSTM+**} & \multicolumn{3}{c}{**LSTM+**} & \multicolumn{3}{c}{**Multi-Head**} & \multirow{2}{*}{**Ours**} \\ \cline{3-13} \cline{6-13} \multicolumn{1}{c}{**Attention**} & & & & & & & & & & & \\ \hline CLAL & 0.80 & 0.82 & 0.85 & 0.88 & 0.81 & 0.83 & 0.86 & 0.89 & 0.82 & 0.84 & 0.87 & 0.90 \\ CLAR & 0.79 & 0.81 & 0.84 & 0.87 & 0.80 &  
algorithm and hardware will likely enhance its real-time processing capabilities, providing a stronger foundation for practical applications.  
Finally, the MR-GCN framework demonstrated superior performance across all datasets for critical metrics such as mean average precision (Map), Recall, and F1. On the Indian dataset, the framework achieved scores of 0.94, 0.92, and 0.93, respectively. On the Apollo dataset, Map improved from 0.87 to 0.94, Recall from 0.85 to 0.92, and F1 from 0.86 to 0.93. On the KITTI dataset, Map increased from 0.87 to 0.94, Recall from 0.86 to 0.92, and F1 from 0.86 to 0.93. These results demonstrate the adaptability and prediction accuracy of the MR-GCN framework in diverse traffic scenarios, showcasing its significant advantages and reliability in practical applications.