## 4 Simulation and Analysis
#### 4.1.5 Results
Case 4: \(A_{4}\) Recognition Network  
The lateral maneuver intention parameter \(A_{4}\) characterizes the HGV lateral force. Since the lateral maneuver of the HGVs relies on the adjustment of bank angle, the onboard infrared sensor cannot precisely recognize the change of the bank angle. Hence, the network input still uses five parameters related to the lateral maneuver intention (\(\theta\), \(\phi\), \(\dot{\theta}\), \(\dot{\psi}\), \(\dot{\psi}\)). Figure 8: Recognition error of A2: (**a**) is the A2 (\(SG\)) recognition error curve and (**b**) is the A2 (\(QEG\)) recognition error curve. Figure 9: Recognition error of A3: (**a**) is the A3 (\(SG\)) recognition error curve and (**b**) is the A3 (\(QEG\)) recognition error curve. Moreover, the lateral maneuver label \(A_{3}\) is added as the reference value for the lateral maneuver direction. From Figure 10a, it can be seen that, compared with \(P_{n}=1\) or \(2\), the \(P_{n}=3\) network got the highest recognition accuracy for \(SG\) trajectories. The mean, median, and minimum values of the root-mean square error of the recognition results are relatively smooth. However, the maximum value is larger because the correlation law between the input parameters and the magnitude of the lateral force is not obvious enough. From Figure 10b, it can be seen that for \(QEG\) trajectories, the maximum, median, and minimum values of the network with \(P_{n}=2\) are slightly smaller than those of the network with \(P_{n}=1\), but the average values are similar. The experiment results of the above intention parameter recognition network show that the stacked LSTM network can recognize the maneuver intention of the HGVs by observations, and the network with \(P_{n}=2,3\) improves the success rate of maneuver intention recognition and accuracy compared to the network with \(P_{n}=1\). For the recognition of longitudinal maneuver type \(A_{1}\), all three parallel forms can achieve a relatively high success rate. For the longitudinal maneuver intentions, the short-time correlation of the \(SG\) trajectory is stronger, whereas the short-time variation of the \(QEG\) trajectory is more flat. The network with \(P_{n}=3\) is more focused on the correlation between short time data, so it is more suitable to be used to recognize the longitudinal maneuver intention of \(SG\) trajectory. In contrast, the altitude change of the \(QEG\) trajectory is gentler. The observed data change less with time in instances of lateral maneuvers; hence, the network with \(P_{n}=2\) can achieve better recognition results. Case 5: The Joint Prediction Network for Maneuver Intentions of \(SG\) Trajectories  
The intention recognition network was used as a data-providing network for the joint prediction network. First, 10% of each trajectory data was used as the observation data. As described in Section 2.3.3, the pre-trained network was loaded and the observed data were used as retraining data to retrain the model in several steps. The intention parameters of the retraining data were recognized by the intention prediction network. The prediction simulation was performed for the HGVs' subsequent intention sequence to compare the prediction accuracy of the intention parameters of the PSLSTM with \(P_{n}=1,2,3\), M-LSTM, and DCBNN. Where M-LSTM and DCBNN have been described in Section 4.1.4. Each prediction required an input sequence length of 50. Since the output length of the intention recognition network was 200, it was divided into four segments, each with a length of 50. The prediction network divided the sequence output into four segments, each with a length  
Figure 10: Recognition error of A4: (**a**) is the A4 (\(SG\)) recognition error curve and (**b**) is the A4 (\(QEG\)) recognition error curve. of 50. Then, these segments and the processed observations were arranged and input into the joint prediction network.