## 4 Experiments
### Ablation Experiments on the Model  
The ablation experiments were performed to evaluate the contribution of each proposed module and to enhance our understanding of the model's functionality. The core components of our model are the Scene Tensor and the Transition Tensor. The former encapsulates critical information pertaining to the immediate environment, while the Transition Tensor incorporates data relevant to the intention of behavior. We define model variants as follows:  
* \(\mathit{Variant}_{1}\): Model (Scene Tensor) It removes the Scene Tensor in the model, causing the model to ignore the impact of environmental information on pedestrian movement. * \(\mathit{Variant}_{2}\): Model (Transition Tensor) It excludes the Transition Tensor from the model, causing the model to disregard the influence of pedestrian intentions on behavior. * \(\mathit{Variant}_{3}\): Model It is a complete model, incorporating both environmental information and the influence of pedestrian intentions on behavior. * \(\mathit{Variant}_{4}\): Model It excludes the Scene Tensor and Transition Tensor from the model and only focuses on the interaction between pedestrians. Table 4 presents the experimental results for our complete model and its variants. It is evident that \(\mathit{Variant}_{4}\) exhibits the poorest performance, primarily because this variant neglects the obstacles around pedestrians and the origin and destination of the pedestrian's \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metric** & **LSTM** & **Social-LSTM** & **Social-Attention** & **Social-GAN** & **STGAT** & **SR-LSTM** & **Social-STGCNN** & **Ours** \\ \hline ADE & 0.61 & 0.67 & 0.78 & 0.76 & 0.59 & 0.51 & 0.44 & 0.41 \\ FDE & 1.31 & 1.40 & 1.56 & 1.52 & 1.23 & 1.10 & 0.79 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results for all models on UNIV dataset. itinerary in the encoding stage. \(Variant_{1}\) ignores the environmental information around pedestrians, which shows that environmental information has an important impact on prediction accuracy. To further explore the effect of information about the surrounding environment on the predictive accuracy of our models, we changed the hyperparameter \(g_{size}\) of the fused Scene Potential Module to explore the optimal distance range that pedestrians can perceive when walking. We conducted experiments on \(g_{size}\) values of 4, 6, 8, 10, and 12, with the results illustrated in Figure 6. The parameter \(g_{size}\) indicates the distance range within which pedestrians observe the surrounding environment while walking. The larger the value of \(g_{size}\), the wider the pedestrian's field of view and the more environmental features are considered; the smaller the value of \(g_{size}\), the fewer environmental features are considered. Figure 6 shows that the model achieves optimal performance with a \(g_{size}\) value of 8. Beyond this value, environmental information becomes redundant, leading to poor results. \(Variant_{2}\) excludes the Transition Tensor and its performance is degraded. This is because pedestrians' travel intentions have an impact on their future trajectories. Therefore, removing this module reduces the effectiveness of the model. Our entire model outperforms \(Variant_{1}\), \(Variant_{2}\), and \(Variant_{4}\) by taking into account all conditions. \(Variant_{1}\) excludes the Scene Tensor and only considers the effect of the intention of pedestrian travel on the motion trajectory. Compared with the complete model \(Variant_{3}\), the performance of the model increases by 52.01% on ADE metrics and 61.51% on FDE metrics; compared with the model \(Variant_{4}\), which does not consider the environmental factors and the intention of pedestrian travel, the performance of the model decreases by 25.75% on ADE metrics and 16.39% on FDE metrics.