## 4 Simulation and Analysis
#### 4.1.5 Results  
Networks \(A_{1}\) and \(A_{3}\) are intention label classification networks; thus, the accuracy rate was used as an evaluation condition to measure the effectiveness of the network in the following form:  
\[P=\frac{\sum\limits_{n=T_{e}}^{n=T_{e}}\sigma_{n}}{T_{e}-T_{s}} \tag{27}\]  
where \(\sigma_{n}\) is the success mark, with 1 representing successful and 0 representing unsuccessful. \(T_{s}\) and \(T_{e}\) are the start time and end time, respectively. The other two intention networks, \(A_{2}\) and \(A_{4}\), are regressor networks, and the root mean square error (RMSE) is used to measure the accuracy of parameter regression. For specific details of the RMSE, please refer to [9, 43]. The specific form is as follows:  
\[R=\sqrt{\frac{1}{T_{e}-T_{s}}\sum\limits_{n=T_{e}}^{n=T_{e}}\left(y_{p}^{n}-y_ {r}^{n}\right)^{2}} \tag{28}\]  
where \(y_{p}^{n}\) and \(y_{r}^{n}\) are the parameter regression values and the parameter real values, respectively. The test comparison results of the intention network are explained below. Based on the same training and test data, the accuracy of the stacked networks with the numbers of parallel networks \(P_{n}=1,2,3\) are compared, and the average, minimum, middle, and maximum values of the accuracy calculation results act as the basis for comparison. Figure 6: Network Training Loss: (**a**) is recognize network training loss and (**b**) is prediction network training loss. Case 1: \(A_{1}\) recognition network  
\(A_{1}\) marks the longitudinal maneuver form of the HGV in the process of determining the maneuver intention of the HGV. The input of the \(A_{1}\) recognition network includes six parameters: \(h\), \(h\), \(v\), \(\vartheta\), \(\gamma\), and \(\dot{\gamma}\). The output of the \(A_{1}\) recognition network is divided into two labels, \(QEG\) and \(SG\). As shown in Figure 7, since the longitudinal maneuver intention of the HGV has apparent patterns in the parameter representation, the recognition accuracy is approximately 100% for the parallel network numbers \(P_{n}=1\) and 2. Therefore, it ensures high learning efficiency and prevents the overfitting phenomenon at the same time. Case 2: \(A_{2}\) Recognition Network  
Section 2.1 shows that there are two different laws for \(A_{2}\) when \(A_{1}\) is 0 (\(SG\)) or 1 (\(QEG\)). The \(A_{2}\) recognition network is divided into \(A_{2}(SG)\) and \(A_{2}(QEG)\) recognition networks. Since \(A_{2}\) is also a parameter characterizing the longitudinal maneuver intention of the HGV, the input of the \(A_{2}\) recognition network is the same as that for \(A_{1}\). The parameter complexity and temporal correlation of \(A_{2}\) are higher than those of \(A_{1}\). The longitudinal dynamics of the HGV of \(SG\) trajectory changes into an oscillating trend. The root means the square error of the network recognition result is the smallest, as shown in Figure 8a. As shown in Figure 8b, the network with \(P_{n}=2\) has the best recognition accuracy for the vehicle of \(QEG\) gliding, as the longitudinal dynamics of the vehicle change gently and regularly. Figure 7: A1 recognition rate. Case 3: \(A_{3}\) Recognition Network  
The lateral maneuver intention label \(A_{3}\) is a classification recognition label to recognize the lateral maneuver direction of the HGV. The input of the \(A_{3}\) recognition network includes five parameters: \(\theta\), \(\phi\), \(\dot{\theta}\), \(\dot{\phi}\) and \(\dot{\psi}\). The output of the recognition network with \(A_{3}\) is classified into three categories: left maneuver, right maneuver, and no lateral maneuver. Since the laws of the observed parameters differ greatly when the longitudinal intention types are different, the prediction network for the lateral maneuver intention \(A_{3}\) still needs to be divided into \(SG\) and \(QEG\). As shown in Figure 9a,b, although the recognition results of the three network forms are close, it can still be seen that the minimum value of the network with \(P_{n}=3\) evaluation results is greater.