## 4 Experiment and Analysis
### Analysis of Results
#### 4.4.3 Results of Ablation Experiments  
To validate the effectiveness of the social attention module and the scene information module, we conducted experiments by removing both modules from the original model. Table 3 presents the comparative results of Average Displacement Error (ADE) and Final Displacement Error (FDE) for models with different configurations, while Figure 10 visualizes the results of the ablation experiment. In this paper, we selected the following three models for comparison: the SGAN model, which does not utilize pedestrian interaction information for predictions; the SIGAN  
Figure 10: Comparison of trajectory prediction of ablation study, where (**a,b**) are from the hotel dataset, (**c**) from the zara1 dataset, (**d**) from the Univ dataset, (**e**) from the ETH dataset, and (**f**) from the zara2 dataset. (**a**) shows pedestrians avoiding stationary pedestrians; (**b**) shows pedestrians avoiding pedestrians on both sides; (**c**) shows pedestrians walking in pairs; (**d**) shows pedestrians walking around stationary obstacles; (**e**) shows pedestrians adjusting their paths in the face of a dense crowd to avoid a collision; and (**f**) shows pedestrians walking along the edges of a car. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**ADE/FDE (Meter)**} \\ \cline{2-4}  & **SGAN** & **SIGAN** & **SISGAN** \\ \hline ETH & 0.67/1.13 & 0.79/1.43 & **0.63/0.95** \\ \hline hotel & 0.72/1.61 & **0.58**/1.21 & 0.58/1.62 \\ \hline Univ & 0.61/1.28 & 0.65/1.41 & **0.50/1.10** \\ \hline zara1 & 0.34/0.71 & **0.32**/0.80 & **0.31**/**0.68** \\ \hline zara2 & 0.42/0.84 & 0.42/0.78 & **0.30**/**0.73** \\ \hline Average & 0.58/1.19 & 0.51/1.10 & **0.46**/**1.01** \\ \hline \hline \end{tabular} Note: the bold data in the table are the best results predicted by the model.  
\end{table} 
Table 3: The Average Displacement Error (ADE) and Final Displacement Error (FDE) of models with different modules on ETH and UCY. model, which employs only the interaction attention module; and the SISGAN model, which simultaneously uses both the interaction attention module and the environmental information module. This comparison is intended to validate the effectiveness of the proposed interaction attention module and environmental information module. The results of the ablation experiments are presented in Table 3.  
From Table 3, it is evident that the overall prediction performance of the SIGAN model is better than that of the SGAN model. However, the prediction results of the SIGAN model on the ETH dataset are inferior to those of the SGAN model. This discrepancy can be attributed to the complexity introduced by the extensive calculations of pedestrian interactions within the SIGAN model. The ETH dataset contains a significant number of static obstacles but a relatively low pedestrian density, which leads to the complex network model actually degrading trajectory prediction performance. Additionally, we observe that the SISGAN model demonstrates improved prediction results compared to the SIGAN model, particularly in the Univ and zara2 datasets, which feature a greater presence of static and dynamic obstacles. These results confirm the effectiveness of the interaction attention module and environmental information module designed in this paper. As illustrated in Figure 10, panel (a) shows the movement of pedestrians avoiding those in front of them. It is evident that the SIGAN model presented in this paper demonstrates a more pronounced ability to avoid pedestrians compared to the SGAN model. Panel (b) indicates that the trajectory predictions from both the SGAN and SIGAN models are largely similar in sparse scenarios. In panel (c), we observe that the SIGAN model yields relatively smaller errors in companion situations. Panel (d) highlights that, while there are no significantly large errors in the final predicted positions, the prediction process itself does not achieve the desired accuracy.