# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 4 Experiment and Results
### Qualitative Analysis  
Based on the quantitative analysis results, our proposed model demonstrates promising performance on the ETH and UCY datasets. In this section, we conduct qualitative analysis through trajectory prediction visualizations. Representative predicted trajectories are selected to illustrate the effectiveness and rationality of our approach. A systematic qualitative comparative analysis is implemented to evaluate the multi-model trajectory prediction performance. Taking the ETH test dataset as an example, we randomly select pedestrian trajectories for visual predictive analysis across four models.  
As illustrated in Figure 6, each row from top to bottom corresponds to a distinct model, while each column from left to right represents the same pedestrian. The models are arranged vertically as DSGAT, Social-TAG, Social-STGCNN, and Social-GAN from top to bottom. The selected pedestrians (left to right) are numbered 62, 113, and 181. Observed trajectories are depicted as blue dotted lines, ground truth trajectories are depicted as yellow cross-marked lines, and model predictions are visualized through probability density  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **AVERAGE ADE** & **AVERAGE FDE** \\ \hline M1(DGCN) & 0.41 & 0.68 \\ M2(Weighting Factor) & 0.44 & 0.73 \\ M3(Addition) & 0.47 & 0.78 \\ M4(WSGAT) & 0.39 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiments on different calculation methods for directed weighted graphs.  
clouds. The darker regions in these clouds indicate higher probability zones for pedestrian movement paths, effectively reflecting the spatial anticipation patterns.  
Through the probability density visualization in Figure 6, it is evident that the DSGAT model proposed in this study exhibits significant advantages in estimating the trajectory probability distributions. Its generated trajectories accurately capture the overall movement trends of pedestrians. This advantage primarily stems from the model's effective integration of multidimensional pedestrian interaction information, including spatial relative positions, movement velocities, directions, and group characteristics, enabling the precise modeling of the trajectory probability distributions.  
Comparative experiments show that while Social-STGCNN remains competitive in short-term predictions (t < 1.2 s) using Euclidean-based social graph convolution, its over-simplified social feature representation causes a significant deterioration in the direction and displacement estimation accuracy during medium- to long-term predictions (t \(\geq\) 2.4 s). Similarly, Social-TAG's exclusion of velocity features results in a 28.6% reduced effective prediction radius, manifesting a pronounced performance decay in the later prediction phases.  
Figure 6: The figure shows the predicted trajectory distribution. The dotted line represents the observed trajectory, and the cross line represents the true trajectory.  
Our comprehensive analysis confirms that the proposed method achieves more rational trajectory distribution modeling through probability density estimation, not only accurately capturing the motion trend characteristics but also maintaining stable performance over temporal sequences. These results substantiate the efficacy of multimodal social feature fusion strategies for pedestrian trajectory prediction in complex scenarios.