# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
## 4 Experimental Results and Analysis
### Performance Comparison
#### 4.2.2 Qualitative Evaluation  
As shown in Table 1 and Figure 6, our method outperforms nine state-of-the-art trajectory prediction methods and achieves the best performance in three evaluation metrics. Among them, the lower the values of MR (k = 6), MR (k = 1), and brier-minFDE (k = 6), the better the performance. [PERSON] [2] uses a CNN-LSTM architecture, where LSTM \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method \& Rank** & **MR (k = 6) \(\downarrow\)** & **MR (k = 1) \(\downarrow\)** & **Brier-minFDE (k = 6) \(\downarrow\)** \\ \hline MTGNet (ours) & **0.17** & **0.60** & **1.99** \\ VI LaneIter & 0.19 & 0.60 & 2.00 \\ GoRela [49] & 0.22 & 0.66 & 2.01 \\ OPPred [50] & 0.18 & 0.61 & 2.03 \\ QCNet [19] & 0.21 & 0.60 & 2.14 \\ THOMAS [2] & 0.20 & 0.64 & 2.16 \\ Autowise. AI(GNA) & 0.29 & 0.71 & 2.45 \\ vilab & 0.29 & 0.71 & 2.47 \\ LGU & 0.37 & 0.73 & 2.77 \\ drivingfree & 0.49 & 0.72 & 3.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of three widely used evaluation metrics. kis the number of predicted trajectories for calculating the evaluation metrics (MR (k = 6) \(\downarrow\), MR (k = 1) \(\downarrow\), brier-minFDE (k = 6) \(\downarrow\)) on the Argoverse 2.0 test dataset. “\(\downarrow\)” indicate that larger is better and smaller is better, and the best results are in bold. Figure 5: Train loss value during pre-training. processes time series data and convolutional social pooling captures the interdependencies of all vehicle movements in the scene. However, in complex traffic scenarios, using grids to model lanes is clearly unreasonable, and the limitations of LSTM lead to unsatisfactory results. The query-centric encoding paradigm of QCNet [19] establishes a local spacetime coordinate system for each scene element, enabling computation reuse and improving inference efficiency. However, it may insufficiently model global spacetime dependencies in complex interaction scenarios. Although the factorized attention mechanism reduces computational complexity from \(\mathrm{O}\left(\mathrm{AT}^{2}\right)\) to \(\mathrm{O}\left(\mathrm{AT}\right)\), significantly minimizing redundant calculations, it may also lead to losses in deep interaction information across time steps and agents, thereby affecting the model's ability to capture long-term dependencies. For example, in scenarios with dense traffic flow or highly interactive agent trajectories, this local encoding strategy may fail to fully integrate dynamic correlations across the global scope, limiting prediction accuracy. In contrast, MTGNet uses Transformer to process time series data and reduce noise, and employs GCN instead of CNN to process graph-structured data in high-interaction scenarios, enabling it to better capture the interaction between agents. MTGNet (ours) achieves the same value in the \(MR\)\((k=1)\) metric and the best results in the \(MR\)\((k=6)\) and \(Brier-minFDE\)\((k=6)\) metrics. As shown in Table 2, MTGNet (ours) outperforms HiVT [4] in both prediction accuracy and parameter efficiency. On the \(Brier-minFDE\)\((k=6)\) metric, MTGNet achieves 1.94, surpassing HiVT's 2.01, which highlights its superior ability to capture critical interaction features. In terms of model size, MTGNet employs 2148 K parameters--20% fewer than HiVT's 2546 K parameters--demonstrating a more compact architecture. In computational complexity analysis, HiVT reduces multi-agent interaction complexity to \(\mathrm{O}\left(\mathrm{NT}^{2}+\mathrm{TNK}+\mathrm{NL}\right)\)[4] via spatiotemporal decomposition and local region constraints, where N is the number of agents, T is the number of the time steps, K\((\mathrm{K}<\mathrm{N})\) is the restricted neighbor interaction range, and L\((\mathrm{L}<\mathrm{T})\) is the limited local time step range.