## 5 Experiments and Analysis
### Comparison with Other Existing Models  
First, a comparison of predictive metrics of several models in the Argoverse 1 motion forecasting dataset is completed. Generalized benchmark models such as LaneGCN, DenseTNT, and SceneTransformer, which are excellent performers in the field of vehicle trajectory prediction, as well as new models such as THOMAS and HcteroGCN, which have made breakthroughs in prediction metrics in recent years, were selected as the comparison targets.  
LaneGCN uses a novel structured map representation instead of the traditional grid map and develops a fusion network to learn lane graph features and interactions between vehicles and maps [31]. DenseTNT transforms the trajectory prediction problem into a probability problem for predicting the future distribution of the driving space, outputting multiple possible trajectories for vehicles and the corresponding probabilities for their selection [32]. SceneTransformer focuses on the joint prediction of multiple predictive entities, incorporating attention mechanisms and employing a masked sequence modeling strategy to enhance model performance [33]. THOMAS proposes a joint multi-agent trajectory prediction framework to efficiently predict the future motion trajectories of multiple targets simultaneously [34]. HcteroGCN introduces a heterogeneous graph convolutional recursive network to learn various interactions and spatiotemporal information in the scene, achieving higher prediction accuracy on public datasets [35].  
The results of the comparative experiments are shown in Table 5 below in terms of the metrics minADE (K = 1) and minFDE (K = 1). From the comparison results, it is evident that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Best\_Epoch** & **RCL** & **KLD** & **ADL** & **Test ADE** \\ \hline Before & 80 & 550.53 & 0.5568 & 161.64 & 2.6188 \\ After & 78 & 299.23 & 0.3172 & 75.67 & 2.5018 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model prediction indicators before and after adding road feature vectors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experiment** & **Test ADE** \\ \hline Complete improved model & 1.0127 \\ Remove the positional encoding layer alone & 1.0630 \\ Remove the dropout layer alone & 1.1340 \\ Remove the multi-head attention layer alone & 1.1544 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ablation experiment results for the improvements made to the CVAE model.  
both the minADE and minFDE of our proposed model outperform the other five compared trajectory prediction models.  
Additionally, based on the Argoverse 2 motion forecasting dataset, we also completed a prediction metric comparison between our proposed model and some models that performed well on this dataset. For instance, HcteroGCN proposes a heterogeneous graph convolutional recursive network to learn various interactions and spatiotemporal information in the scene, achieving higher prediction accuracy on the Argoverse 2 dataset. The comparative results are shown in Table 6 below.  
The experimental results demonstrate that our model's prediction capability has significantly improved compared to THOMAS, and the final prediction performance is also superior to HcteroGCN. Additionally, thanks to multiple effective enhancements to the driving endpoint prediction module, it can be observed that the improvement of minFDE (7%) for calculating the distance between the final predicted position and the real position is significantly better than that of minADE (3.4%), which is also in line with the results of previous ablation experiments.