## 4 Experiments
### Qualitative Results  
#### 4.3.1 Social Attention with Motion Features  
The role of the social attention module is demonstrated in Figure 5 through a visualization of the attention weights at various time steps. The target pedestrian trajectory is depicted by lines without numeric labels and circles, with the arrow indicating the forward direction. At each time step, the pedestrian's position is denoted by black dots, and the distance traveled during a time step is illustrated by the length of the line segment between two consecutive dots, thereby reflecting the pedestrian's speed. The remaining trajectories of the neighboring pedestrians are indicated using colored circles and numeric labels. The size of each circle reflects the social attention weight at that moment. The weights of the first to fourth time steps are represented using purple, blue, orange, and red circles, respectively. Figure 5a illustrates that pedestrians 1 and 3 move toward the target pedestrian head-on, while pedestrians 2 and 4 move in the same direction. Therefore, pedestrians 1 and 3 are assigned higher weights. As pedestrian 1 is the closest to the target pedestrian, it is assigned the highest attention weight. Figure 5b shows a similar attention-weight distribution in a denser crowd, where pedestrians moving head-on are assigned larger weights, and the closer the distance, the higher the weight. In Figure 5c, it can be seen that although pedestrian 3 moves in a head-on direction, pedestrian 1 moves quickly and is assigned larger weights at time steps 3 and 4. In Figure 5d, it can be seen that pedestrians 1 and 2 move slowly and are far from the target pedestrian, making their influence negligible. Pedestrian 3, being the closest to the target pedestrian and gradually approaching it, is assigned the highest weight. On the other hand, pedestrians 4 and 5 have a similar impact on the target pedestrian since they walk side by side. In Figure 5e, it can be seen that pedestrians 1 and 3 have a greater influence on the target pedestrian. Due to the possibility of a head-on collision, pedestrian 1 is assigned a larger attention weight at time step 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Dataset**} & **SSA-GAN** & **SSA-GAN** & **Complete** \\  & & **without** & **without Social** & **SSA-GAN** \\  & & **Comprehensive** & **Attention** & **SSA-GAN** \\ \hline \multirow{4}{*}{ADE} & ETH-Eth & 0.79 & 0.76 & **0.68** \\  & ETH-Hotel & **0.66** & 0.68 & 0.69 \\  & UCY-Univ & 0.58 & 0.63 & **0.55** \\  & UCY-Zara1 & 0.31 & 0.30 & **0.30** \\  & UCY-Zara2 & 0.38 & 0.39 & **0.36** \\  & AVG & 0.54 & 0.55 & **0.52** \\ \hline \multirow{4}{*}{FDE} & ETH-Eth & 1.50 & 1.48 & **1.44** \\  & ETH-Hotel & 1.60 & 1.58 & **1.55** \\  & UCY-Univ & 1.23 & 1.25 & **1.22** \\  & UCY-Zara1 & 0.64 & 0.65 & **0.63** \\  & UCY-Zara2 & 0.80 & 0.82 & **0.75** \\  & AVG & 1.15 & 1.15 & **1.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the ablation study. The three compared models are the full SSA-GAN, the SSA-GAN without comprehensive motion features, and the SSA-GAN without social attention. The prediction error metrics are the ADE and FDE in meters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Dataset** & **S-GAN** & **SSA-GAN** \\ \hline \multirow{2}{*}{minADE} & SMTD-Crossroad & 0.68 & **0.63** \\  & SMTD-Plaza & 0.75 & **0.69** \\ \hline \multirow{2}{*}{minFDE} & SMTD-Crossroad & 0.75 & **0.72** \\  & SMTD-Plaza & 0.81 & **0.78** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of our model with S-GAN on the SMTD. The prediction error metrics are the minADE and minFDE in meters. In Figure 5f, it can be seen that pedestrians 2-8 walk along with the target pedestrian, maintaining their relative positions. Their relative speed is nearly zero, and the attention mechanism assigns them similar weights.