## 4 Experimental Evaluation and Analysis
### Quantitative Analysis  
In order to evaluate the accuracy of our proposed model, some excellent vehicle trajectory prediction models were selected for quantitative comparison. These were:  
1. CV: this baseline uses a constant velocity Kalman filter to predict the trajectory of a single vehicle and does not consider the interaction between the predicted vehicle and the surrounding neighbor vehicles. 2. V-LSTM: this baseline learns the information hidden in the historical trajectory data based on an LSTM encoder-decoder, and then generates the trajectory distribution of the target vehicle. V-LSTM also does not consider the interdependencies among vehicles. 3. C-VGMM+VIM [27]: this baseline first estimates the maneuver intentions based on a hidden markov model (HMM), and then generates the future trajectory based on the variational gaussian mixture models. 4. MATF [28]: this baseline encodes the historical trajectory data of vehicles and the information of the driving scene into a multi-agent tensor, and then applies CNNs to extract spatial interactions, decode, and predict the trajectories of all vehicles based on LSTM. 5. CS-LSTM: this baseline uses convolutional social pooling as an improvement to social pooling layers to robustly learn spatial interdependencies in vehicle motion. 6. GRIP: this baseline uses GCNs to model the spatial-temporal interactions among vehicles and generate the trajectories of all vehicles based on an LSTM encoder-decoder. We compared the prediction errors of our model with the above baselines on I-80 and US-101 datasets. The RMSE values are shown in Table 1, and the comparison line chart is shown in Figure 6. In Table 1, we can see that the RMSE values of the VTP-GCN model are lower in all prediction horizons, demonstrating the superior spatial-temporalinteraction feature modeling ability and future trajectory inference ability of our proposed model. In addition, we found that the baselines (CV, V-LSTM, and C-VGMM-VIM) have larger prediction errors than the models that consider interdependencies, indicating that the interaction information is crucial for the accurate prediction of trajectories. The RMSE values of the models that consider the spatial interactions in the driving scene are reduced, but the trajectory prediction accuracy is still not as good as that of the models considering the spatial-temporal interaction information, showing the necessity of temporal interaction modeling for vehicle trajectory prediction. Although both are vehicle trajectory prediction models based on spatial-temporal interaction modeling, the VTP-GCN model that we proposed attained an 8% average accuracy improvement compared with the GRIP model. That is because we not only improved the modeling of spatial interactions based on the proposed SIC, but also constructed a temporal graph based on raw vehicle data for extracting temporal interactions. The above modifications make the spatial-temporal interaction features obtained by fusion more adequate, while GRIP only extracts interaction features based on the single spatial graph. Therefore, the more effective spatial-temporal interaction feature modeling method helps improve the accuracy of vehicle trajectory prediction. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Prediction** & **CV \({}^{*}\)** & **V-LSTM \({}^{*}\)** & **C-VGMM-VIM \({}^{*}\)** & **MATF \({}^{*}\)** & **CS-LSTM \({}^{*}\)** & **GRIP \({}^{*}\)** & **VTP-GCN (\(\alpha\)GRIP)** \\ \hline
1 & 0.73 & 0.68 & 0.66 & 0.67 & 0.62 & 0.64 & **0.55** (\(\downarrow\)14\%) \\
2 & 1.78 & 1.65 & 1.56 & 1.51 & 1.29 & 1.13 & **0.99** (\(\downarrow\)12\%) \\
3 & 3.13 & 2.91 & 2.75 & 2.51 & 2.13 & 1.80 & **1.56** (\(\downarrow\)13\%) \\
4 & 4.78 & 4.46 & 4.24 & 3.71 & 3.20 & 2.62 & **2.36** (\(\downarrow\)9\%) \\
5 & 6.68 & 6.27 & 5.99 & 5.12 & 4.52 & 3.60 & **3.59** (\(\downarrow\)0\%) \\ \hline Average & 3.42 & 3.30 & 3.04 & 2.70 & 2.35 & 1.96 & **1.81** (\(\downarrow\)8\%) \\ \hline \hline \end{tabular}
\end{table}