## 5 Experiments and Analysis
### Visualization of Predictions under Different Scenarios  
In order to visualize the prediction results under different scenarios, this paper also developed a tool that can complete the visualization of road maps and all vehicle trajectories (both predicted and real values) for specified scenarios. In this section, the prediction results under several different types of typical scenarios are shown and analyzed to demonstrate the actual prediction effect of our model. The selected scenarios are mainly based on different road conditions (merging, straight lines, off-ramps, etc.) and traffic flows.  
The exit ramp and its surrounding area on the expressway are accident-prone sections. Figure 9 depicts a typical exit ramp scenario. In the image, the gray lines represent known historical vehicle trajectories. The green lines represent the actual results, the red lines represent the model's prediction results. The blue dots represent the results from the endpoint prediction module. This scenario captures five vehicles traveling in the same direction, with two vehicles driving in the leftmost lane. The real trajectory of the leading vehicle in this lane indicates that the vehicle is going to exit from the ramp. The model accurately predicts the lane-changing behavior of this vehicle as it exits. The predicted future driving endpoint closely matches the actual result for this vehicle. Moreover, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **minADE (K = 1)** & **minFDE (K = 1)** \\ \hline THOMAS & 1.96 & 4.71 \\ GoReLa & 1.82 & 4.62 \\ GANNet & 1.78 & 4.48 \\ BANet & 1.79 & 4.61 \\ HcteroGCN & 1.79 & 4.53 \\ CVAE-VGAE (this paper) & 1.73 & 4.21 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of metrics for trajectory prediction models based on the Argoverse 2 motion forecasting dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **minADE (K = 1)** & **minFDE (K = 1)** \\ \hline LaneGCN & 1.71 & 3.78 \\ DenseTNT & 1.68 & 3.63 \\ SceneTransformer & 1.81 & 4.06 \\ THOMAS & 1.67 & 3.59 \\ HcteroGCN & 1.62 & 3.52 \\ CVAE-VGAE (this paper) & 1.59 & 3.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of metrics of six trajectory prediction models based on the Argoverse 1 motion forecasting dataset.  
endpoint predictions for all vehicles in this scenario are relatively accurate, demonstrating the effectiveness of conducting endpoint intention inference beforehand.  
The scenario in Figure 10 below is similar to the most common situation of multiple vehicles driving straight on expressways. For straight-driving vehicles with stable operating conditions, our model can more accurately predict the driving endpoints and fit the trajectories. At the same time, for stopped vehicles that are located in the right lane from left to right, their stopping status will also be accurately predicted.  
Predicting turns and lane changes is one of the most challenging situations in trajectory prediction. Figure 11 includes two common turn prediction scenarios. The past trajectory  
Figure 10: Visualization of predictions for a scenario with some vehicles traveling straight.  
Figure 9: Visualization of predictions for a multi-vehicle scenario at an exit ramp.  
of the vehicle above includes part of the turning trajectory, so it is relatively easy to predict its future trajectory. However, the past trajectory of the vehicle below does not show a clear turning process, so it is relatively difficult to make the prediction. In both cases, our model successfully predicts the driving intention, and the prediction results closely match the actual trajectories.