# _M\({}^{2}\)Tames_: Interaction and Semantic Context Enhanced Pedestrian Trajectory Prediction
## 4 Results
### Quantitative Results
Table 3 presents the results for the Stanford Drone Dataset (SDD) in long-term settings, predicting future trajectories for \(t_{f}=30\) s, based on a past motion history of \(t_{p}=5\) s. All reported errors are in pixels, with lower values indicating better performance. _M2 Tames_ achieved an Average Displacement Error (ADE) of 44.85 and a Final Displacement Error (FDE) of 65.97. The proposed model, _M2 Tames_, surpasses all the others, outperforming the best-performing Y-Net [30] by 6.89% (ADE) and 1.12% (FDE). Y-Net introduced a long-term trajectory prediction task with a prediction range of up to 30 s. For benchmarking purposes, the results of Y-Net [30] are assessed using the published source code, while the results for other models come from [30]. For long-term pedestrian trajectory prediction, _M2 Tames_ demonstrates its exceptional performance in extended sequence forecasting by adjusting the \(K_{a}\) parameter. As the \(K_{a}\) value increases, the model not only maintains high accuracy but also significantly reduces ADE and FDE metrics, even in the most challenging long-term prediction scenarios. This reflects the model's effective handling of long-term dependencies. Such performance advantages are particularly suited for applications requiring highly accurate long-term predictions, such as intelligent transportation systems and urban monitoring. This underscores the practical value of _M2 Tames_ in urban planning and security monitoring systems. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **ETH** & **HOTEL** & **UNIV** & **ZARA1** & **ZARA2** & **AVG** \\ \hline Social-LSTM [1] & 1.09/2.35 & 0.79/1.76 & 0.67/1.40 & 0.47/1.00 & 0.56/1.17 & 0.72/1.54 \\ Social-GAN [2] & 0.81/1.52 & 0.72/1.61 & 0.60/1.26 & 0.34/0.69 & 0.42/0.84 & 0.58/1.18 \\ ST-GAT [41] & 0.65/1.12 & 0.35/0.66 & 0.52/1.10 & 0.34/0.69 & 0.29/0.60 & 0.43/0.83 \\ Transformer-TF [42] & 0.61/1.12 & 0.18/0.30 & 0.35/0.65 & 0.22/0.38 & 0.17/0.32 & 0.31/0.55 \\ STAR [40] & 0.36/0.65 & 0.17/0.36 & 0.31/0.62 & 0.26/0.55 & 0.22/0.46 & 0.26/0.53 \\ Trajectron++ [9] & 0.39/0.83 & 0.12/0.21 & 0.20/0.44 & 0.15/0.33 & 0.11/0.25 & 0.19/0.41 \\ Goal-GAN [34] & 0.59/1.18 & 0.19/0.35 & 0.60/1.19 & 0.43/0.87 & 0.32/0.65 & 0.43/0.85 \\ PCNet [11] & 0.54/0.87 & 0.18/0.24 & 0.35/0.60 & 0.22/0.39 & 0.17/0.30 & 0.29/0.48 \\ _V2_-Net [42] & 0.23/0.37 & 0.11/0.16 & 0.21/0.35 & 0.19/0.30 & 0.14/0.24 & 0.18/0.28 \\ MG-GAN [37] & 0.47/0.91 & 0.14/0.24 & 0.54/1.07 & 0.36/0.73 & 0.29/0.60 & 0.36/0.71 \\ D-STGCN [40] & 0.63/1.03 & 0.37/0.58 & 0.46/0.78 & 0.35/0.56 & 0.29/0.48 & 0.42/0.68 \\ MSRL [6] & 0.28/0.47 & 0.14/0.22 & 0.24/0.43 & 0.17/0.30 & 0.14/0.23 & 0.19/0.33 \\ _M2 Tames_ & 0.27/0.39 & 0.11/0.16 & 0.21/0.43 & 0.17/0.26 & 0.15/0.20 & 0.18/0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Short-term pedestrian trajectory prediction on the ETH/UCY dataset. _M2 Tames_, through its innovative multi-head mask temporal attention mechanism and U-Net architecture, demonstrates superior performance in both short-term and long-term pedestrian trajectory prediction tasks. These performance metrics validate the model's effectiveness in understanding pedestrian dynamics and interactions in complex scenarios. Whether in short-term or long-term prediction tasks, _M2 Tames_ provides robust data support and decision-making foundations for applications. Further analysis is conducted on the impact of different spatial layers \(L\) within the multi-layer spatial module on the model's performance. As shown in Figure 5, there is a noticeable downward trend in both Average Displacement Error (ADE) and Final Displacement Error (FDE) with the increase in the number of spatial layers. However, as \(L\) increases, a significant amount of time and space is required for training and testing. Observing the trend, it is evident that the rate of error reduction diminishes. The benefits gained from adding more layers do not justify the additional time and space required. Therefore, this study limits the number of layers to four.