# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 4 Experiments
### Visualization  
#### 4.5.1 Trajectory Prediction Visualization  
We visualized some common interaction scenes in Figure 6, where yellow dot lines represented observed trajectories, and the blue and red lines were the trajectories predicted by STAR [27] and our proposed NaST model, respectively. The green lines were the ground truth. The visualization revealed that our prediction (red dot line) had better tendencies toward the ground truth. The prediction from STAR [27] (blue dot line) had more deviations from the ground truth.  
In Figure 6 scenario (a), two pedestrians were walking in parallel with the same direction. NaST (red dot line) could better match the ground truth compared with STAR (blue dot line). In scenario (b), pedestrians were walking in a perpendicular direction. NaST captured the intention and predicted more accurate directions for each pedestrian. Since one of the pedestrians in scenario (b) had a sharp turn, both methods failed to achieve accurate prediction because only observed trajectory information was given. From scenario (c) and (d), we can see that STAR [27] suffered from the overlap issue with a high possibility  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variants**} & \multicolumn{6}{c}{**Performance (ADE/FDE)**} \\ \cline{2-7}  & **ETH** & **HOTEL** & **UNIV** & **ZARA1** & **ZARA2** & **AVG** \\ \hline NaST-auto & 0.33/0.69 & 0.21/0.45 & 0.31/0.67 & 0.31/0.68 & 0.22/0.54 & 0.28/0.61 \\ NaST (ours) & 0.35/0.62 & 0.15/0.35 & 0.27/0.56 & 0.25/0.56 & 0.19/0.41 & 0.24/0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of non-autoregressive and autoregressive inference pattern on ETH and UCY datasets of ADE/FDE metrics (the lower numerical result is better).  
Figure 6: Trajectory visualization in different scenes. The yellow dot lines represent the observed trajectories, and the blue and red lines are the trajectories predicted by STAR [27] and our proposed model. The green lines are the ground truth. (a) Pedestrians walked in parallel from the same direction. (b) Pedestrians walked in perpendicular direction. (c) Multi pedestrians walked from the same direction. (d) Multi pedestrians walked from different direction.  
of collision, while NaST considered both spatial interaction and temporal tendency to avoid collision.  
STAR [27] also adopted transformers for feature extraction. It only constructed the encoder stack and predicted future locations in an autoregressive pattern. Errors accumulated during the inference procedure and made the final destination prediction deviate largely from the ground truth. Examples are shown in Figure 6a,c. From the pictures in the top of Figure 6a,c, we can see that the final destination predictions by STAR [27] (the last blue dot of each predicted trajectory) were far away from the ground truth (the last green dot of each predicted trajectory). The results were much better for NaST (pictures in the bottom of Figure 6a,c). The final destination predictions by NaST (the last red dot of each trajectory) were much closer to the ground truth (the last green dot of each trajectory).