## 5 Experiment and Analyses
### Quantitative Comparison with Baselines  
To reveal the advantage of the proposed model, some benchmark models are listed here for quantitative comparison.  
CV: single-vehicle trajectory prediction using a Kalman filter at a constant velocity [35].  
IA-KNN: a multi-layer architecture of interaction-aware Kalman neural networks (IaKNNs), which involves an interaction layer for resolving high-dimensional traffic environmental observations and a filter layer for future trajectories' estimation [26].  
S-LSTM: sharing the information between multiple LSTMs to capture the interactions within the neighborhood corresponding to the neighboring trajectories [11].  
GR-LSTM: adopting an LSTM to handle the temporal sequence and a graph representation learning module to precisely represent the spatial interaction between vehicles [27].  
S-GAN: using a recurrent sequence-to-sequence model to observe the motion histories and aggregate the information via a pooling mechanism. The plausible future trajectories are predicted through adversarial training against a recurrent discriminator [28].  
GRIP++: applying a graph to represent the interactions of neighboring objects with several graph convolutional blocks to extract features, while an encoder-decoder LSTM model is designed to make the predictions [29].  
DAM: a dual attention mechanism is introduced for trajectory prediction by analyzing the influence between the neighboring vehicle and target vehicle, which is combined with the temporal hidden trajectory feature of the target vehicle to reduce the uncertainty of the potential trajectory [17].  
The experiment results and the comparisons between benchmarks are listed in Table 1. The CV model has the worst prediction performance when compared with other algorithms both for prediction error and its growth rate, which illustrates that vehicle nonlinear characteristics, different driver behaviors, and environmental changes have a significant effect on the future trajectory changes of vehicles. The rest of the learning-based prediction algorithms enhanced the prediction performance by extracting hidden features from the dataset. Note that the original S-LSTM was used for trajectory prediction of pedestrians. Although it analyzed the relative interactions of different objects, it did not have satisfactory performance for vehicle trajectory prediction, illustrating the significant differences between pedestrian-pedestrian and vehicle-vehicle interaction. GR-LSTM, S-GAN, and DAM used different methods to further explore the potential influence mechanism of spatio-temporal features on the future trajectories of vehicles, all of which improved the prediction accuracy. GRIP++ has the best prediction accuracy among the existing algorithms, revealing the advantage of a graph for the representation of vehicle-vehicle interactions. Compared with the aforementioned methods, our model not only represents the correlation of historical positions of the object vehicle and the surrounding vehicles through the spatio-temporal attention mechanism, but the relative dynamic motion interaction features between vehicles are also extracted through GAT combined with the artificial potential field. Thus, it achieves the lowest average RMSE of 1.45 m for the prediction trajectory, which improves the best baseline (GRIP++) by 5.9%. For the entire prediction horizon, our method also has the better performance at all prediction moments except for at 1 s. It is not all that worse than the best method, and is acceptable for a vehicle decision system.