# _M\({}^{2}\)Tames_: Interaction and Semantic Context Enhanced Pedestrian Trajectory Prediction
## 4 Results
### Quantitative Results  
Table 1 presents the results for the Stanford Drone Dataset (SDD) in short-term settings, specifically with \(t_{p}=3.2\) s and \(t_{f}=4.8\) s. According to the established protocol delineated in Trajnet [32], leave-one-out cross-validation was employed. With 20 prediction samples, \(M^{2}\)_Tames_ achieved an Average Displacement Error (ADE) of 8.02 and a Final Displacement Error (FDE) of 12.31, surpassing all competing models. Notably, it outperformed the latest MSRL [6] by margins of 2.49% for ADE and 8.77% for FDE. The model produces a separate prediction of \(K_{a}\) for each given goal. In short time horizons, because the total path is short, the paths to a given goal are similar to each other. Thus, the model is set at \(K_{a}=1\). In short-term pedestrian trajectory prediction, \(M^{2}\)_Tames_ demonstrates significant advantages through its innovative combination of \(M^{2}\)_Tea_, \(I^{2}\), and the U-Net architectures. This integration enables the model to effectively capture and leverage pedestrians' historical trajectory, particularly in applications involving complex interactions and dynamic environments. Both the ADE and FDE metrics demonstrate that the model achieves lower errors in predicting short-term pedestrian trajectories. This underscores its advanced capability in understanding pedestrian behavior dynamics, as well as their interactions with the environment and other pedestrians. Consequently, \(M^{2}\)_Tames_ exhibits strong practicality and broad application potential in handling short-term prediction tasks. Table 2 presents the results for the short-term setup on the ETH/UCY datasets. Employing the standard protocol from Trajnet [32], which includes leave-one-out cross-validation and uses 20 prediction samples, \(M^{2}\)_Tames_ achieves a mean displacement error (ADE) of 0.18 and a final displacement error (FDE) of 0.28. These metrics surpass those of all competing models, including the recent MSRL. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Year** & **ADE** & **FDE** \\ \hline Social-LSTM [1] & 2016 & 57.00 & 31.20 \\ Social-GAN [2] & 2018 & 27.23 & 41.44 \\ CF-VAE [33] & 2019 & 12.60 & 22.30 \\ Goal-GAN [34] & 2020 & 12.20 & 22.10 \\ P2 TIRL [35] & 2020 & 12.58 & 22.07 \\ PECNET [11] & 2020 & 9.96 & 15.88 \\ SimAug [36] & 2020 & 10.27 & 19.71 \\ MG-GAN [37] & 2021 & 13.60 & 25.80 \\ LB-EBM [38] & 2021 & 8.87 & 15.61 \\ CAGN [38] & 2022 & 9.42 & 15.93 \\ Multiclass-SGCN [39] & 2022 & 14.36 & 25.99 \\ D-STGCN [40] & 2023 & 15.18 & 25.50 \\ MSRL [6] & 2023 & 8.22 & 13.39 \\ \(M^{2}\)_Tames_ & 2024 & 8.02 & 12.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Short-term pedestrian trajectory prediction on the SDD dataset. An analysis of experimental results indicates that the model exhibits exceptional performance on the Hotel, Zara1, and Zara2 datasets. An in-depth examination of these datasets reveals that the model performs particularly well in scenarios involving dense crowds. This superior performance is attributed to the innovative modules introduced, which enable a more precise depiction of interactions among agents, thereby enhancing the model's predictive accuracy. _M2 Tames_ not only achieves technical innovation but also demonstrates significant effectiveness in practical applications. By integrating advanced attention mechanisms, the model not only excels on a single dataset but also maintains a high level of predictive accuracy across various environments and dynamic conditions. Moreover, the model's consistent performance across different scenarios provides strong evidence of its scalability and adaptability for real-world applications. These results offer a new technical pathway for pedestrian trajectory prediction and hold important implications and application value for research in intelligent transportation systems, urban planning, and security monitoring.