## 4 Simulation and Analysis
#### 4.1.5 Results
The comparison results are as follows:  
As shown in Figure 11, the simulation results of the maneuver intention prediction network for \(SG\) trajectory show that the prediction accuracy of the parallel-stacked neural network for the intention parameters is generally higher than that of the series stacked network. Additionally, the parallel network with \(P_{n}=3\) has higher prediction accuracy for the \(SG\) trajectory intention parameters \(A_{1}\), \(A_{2}\), and \(A_{3}\). The three PSLSTM networks have similar prediction accuracy for the intention \(A_{4}\), although the average value of the error at \(P_{n}=2\) is slightly smaller than those of the other two. M-LSTM and DDCNN also have the ability to maneuver intention prediction, but the prediction accuracy is lower than that of PSLSTM. DCBNN has the advantage of dual-channel and bidirectional recurrent network units, and the prediction accuracy is slightly higher than that of M-LSTM. Figure 11: Prediction error of the maneuver intention prediction networks (\(SG\)): (**a**) is the error of A1 prediction, (**b**) is the error of A2 prediction, (**c**) is the error of A3 prediction, and (**d**) is the error of A4 prediction. Case 6: The Joint Prediction Network for Maneuver Intentions of \(QEG\) Trajectories  
The following is a comparison of the networks with \(P_{n}=1,2,3\) in parallel form:  
The longitudinal flight path of the \(QEG\) trajectory is smoother than the \(SG\) trajectory. In contrast, the maneuver intention prediction network is more focused on learning the patterns of the lateral maneuver sequences. As such, the accuracy of the joint prediction results of the \(QEG\) trajectory is generally higher than that of the \(SG\) trajectory. As shown in Figure 12, it can be found that the prediction accuracies of the intention parameters \(A_{1}-A_{3}\) are better when the network parallel number \(P_{n}=2\) of PSLSTM. However, there was no significant difference in the prediction accuracies of the three PSLSTM for \(A_{4}\). The difference in prediction accuracy between M-LSTM and DCBNN is smaller, and DCBNN has an advantage in the prediction of \(A_{2}\) and \(A_{4}\), but the accuracy is still lower than that of PSLSTM. In summary, the test results show that compared with the series stacked network, the parallel-stacked neural network has improved the prediction accuracy, especially in the maneuver intention-related prediction of the HGVs. The PSLSTM with \(P_{n}=3\) can input three consecutive state vectors at the same time, so the network is more focused on  
Figure 12: Prediction error of the maneuver intention prediction network (\(QEG\)): (**a**) is the error of A1 prediction, (**b**) is the error of A2 prediction, (**c**) is the error of A3 prediction, and (**d**) is the error of A4 prediction. the correlation between short-term data. The PSLSTM with \(P_{n}=3\) shows advantages in the intention recognition and prediction of \(SG\) trajectories. In the PSLSTM with \(P_{n}=2\), considering two consecutive state vectors, the long-term association in memory also plays a positive role in the results. The PSLSTM with \(P_{n}=2\) shows more advantages in \(QEG\) trajectories intention recognition and prediction.