## 4 Experimental Evaluation and Analysis
### Ablation Study  
(1) Spatial Graph with Different Weighted Adjacency Matrices  
In this paper, we propose the concept of spatial interaction coefficient and use it as the kernel function to construct a weighted adjacency matrix of the spatial graph to mark the intensity of the spatial interactions between two vehicles within the driving scene. To verify the effectiveness of the above method, the spatial interaction kernel function in the prediction model was modified to the reciprocal of the Euclidean distance. As shown in Table 2, the RMSE values of the SIC-based model are smaller than those of the reciprocal-of-Euclidean-distance-based model at all prediction horizons. Specifically, compared with VTP-GCN, the average RMSE value of the model based on the reciprocal of the Euclidean distance is 5% higher, which proves the effectiveness of the proposed spatial interaction kernel function SIC. The comparison line chart is shown in Figure 7a.  
(2) Temporal Graph with Different Weighted Adjacency Matrices  
In order to effectively extract the temporal dependencies of the vehicles, we propose two methods to construct a weighted adjacency matrix of the temporal graph: one based on the reciprocal of the time interval, and one based on the self-attention mechanism. Generally, we think that the influence of the vehicle historical motion state on its current trajectory will weaken as the time interval increases, so we used the reciprocal of the time interval to mark the intensity of temporal interactions. At the same time, the temporal dependency of vehicle is unidirectional, that is, the future trajectory of the vehicle cannot affect the past movement state of the vehicle. Therefore, the weighted adjacency matrix of the temporal graph is defined as:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prediction** & **1** & **2** & **3** & **4** & **5** \\
**Horizon (s)** & 0.63(+0.08) & 1.08(+0.09) & 1.65(+0.09) & 2.43(+0.07) & 3.73(+0.04) \\ _SIC_ & **0.55** & **0.99** & **1.56** & **2.36** & **3.59** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of RMSE values for models with different spatial weighted adjacency matrices. Data are converted into meters.  
Figure 7: Prediction errors of models with different spatial weighted adjacency matrices (**a**) and different temporal weighted adjacency matrices (**b**).  
\[A_{tmp\_i}=\begin{pmatrix}0&\frac{1}{|\Delta T_{1,2}|}&\frac{1}{|\Delta T_{1,3}|}& \cdots&\frac{1}{|\Delta T_{1,N}|}\\ 0&0&\frac{1}{|\Delta T_{2,3}|}&\cdots&\frac{1}{|\Delta T_{2,N}|}\\ 0&0&0&\cdots&\frac{1}{|\Delta T_{3,N}|}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\cdots&0\\ \end{pmatrix} \tag{20}\]  
where \(\Delta T_{k,q}\) is the difference between the historical time instant \(k\) and \(q\). We describe in detail the matrix construction method based on the self-attention mechanism in Section 3.1. (2), and the RMSE values of the prediction models based on the above two methods trained separately are shown in Table 3. The prediction accuracy of the model based on the self-attention mechanism is higher than the other. Specifically, compared with VTP-GCN, the average RMSE value of the model based on the reciprocal of the time interval is 12% higher, meaning that the temporal interaction strength of the vehicle is not linearly related to the time interval, and the method based on self-attention can more fully represent the intensity of temporal interaction. The comparison line chart of the two methods is shown in Figure 7b.