## 5 Experiments and Analysis
### Ablation Experiments  
To independently verify the effectiveness of the improvements made to the original baseline model, we designed a set of comparative experiments that did not integrate the map feature extraction sub-model. A random sample of 10,000 scenes from the Argoverse 2 motion forecasting training set was selected as the training set, and 1000 scenes were used as the test set. Training was conducted for 100 epochs on both the unmodified baseline model and the improved model, and the best prediction results on the test set are summarized in Table 2 below:  
From the experiment results, it can be seen that the predictive performance of the model improved significantly after multiple enhancements, surpassing the previous model. The overall trajectory prediction accuracy increased by over 10%. In order to further demonstrate the effectiveness of each improvement on the CVAE baseline model, comprehensive ablation experiments were designed. The ablation experiments were performed on identical small sample datasets and none of them incorporated the map feature extraction sub-model. The experiment details and results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Time/s** & **Before (1 Titan X)** & **Optimized (8 Titan X)** \\ \hline
20 Epoch & 4218.33 s (1 h 10 min) & 289.73 s (4.83 min) \\ Average per Epoch & 210.92 s (3 min 31 s) & 14.49 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of training duration before and after multi-process optimization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Learning\_Rate** & **Fdim** & **Num\_Head** & **Test ADE** & **Test aveFDE** \\ \hline OLD & 0.0003 & - & - & 0.7256 & 1.7130 \\ NEW & 0.0005 & 64 & 8 & 0.6515 & 1.6843 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of predictive indicators before and after improvement of the main model for trajectory prediction. The two models used their respective best hyperparameter combinations.  
The results verify that removing any additional layer added to the autoencoder will lead to a significant decrease in the predictive metrics. In particular, removing the multi-head attention layer will result in the largest decrease, indicating that the multi-head self-attention mechanism indeed effectively enhances the model's ability to capture information from sequential data.  
Finally, in order to demonstrate that adding the feature vectors generated by the map feature extraction sub-model to the main model is an effective improvement, a comparative experiment was conducted. This experiment was trained for 100 epochs with and without map feature vectors, respectively, under the condition that the irrelevant factors such as the amount of data and hyperparameters were consistent. The optimal prediction performance obtained is recorded in the following Table 4:  
In addition to the ADE of the entire test set, Table 4 also displays the loss results of different parts of trajectory prediction during the training process. RCL represents trajectory error. KLD represents KL divergence error, and ADL represents endpoint prediction error. It is evident that the losses in each part have been effectively reduced after incorporating the map information. This suggests that the fusion of VGAE-based sub-model has led to an effective improvement in both the prediction of driving endpoints and the fitting of intermediate trajectories.