## 2 Establishing and Recognizing the Maneuver Intention of HGVs
### Parallel-Stacked LSTM Neural Network
\begin{array}{l}\iota_{n,13,5}=FC_{1-3}(Seq_{n}),\\ \iota_{n,2A,6}=BN_{1-3}(\iota_{n,13,5}),\\ \left[\iota_{n,L1-19},M_{n,L1-19}\right]=LS_{19}(\iota_{n-1,L1-19},M_{n-1,L1-1 9},\iota_{n,1,L1,L2,2,3,L4,3,L5,L6,4,L7,L8}),\\ \iota_{n,7}=FC(\iota_{n,19}),\\ P(A_{k}^{1:N}|Seq_{(1:N)})=S_{f}(\iota_{n,7}),k=1,3\\ A_{k}^{1:N}=Cl\left(P(L\big{|}Seq_{(1:N)})\right),k=1,3\\ A_{k}^{1:N}=\text{Re}(\iota_{n,7}),k=2,4\\ n=1,2,\cdots,N\end{array} \right. \tag{12}\]  
where \(S_{f}(x)\) is a fully connected layer with the softmax function. After the independent recognition of the above network, the maneuver intention sequence of the HGV can be obtained. The intention sequence and observations are integrated and put into the joint maneuver intention prediction neural network. This network incorporates the learning of the four intention parameters and the correlation among the observations to predict the subsequent maneuver intention of the HGV. The structure of the joint prediction algorithm using the above network is shown in Figure 3. To obtain the final maneuver intention prediction results, firstly, the observation information is preprocessed to fit the requirements of the maneuver intention recognition network. Then, the intention labels and intention parameters of HGVs are recognized based on the PSLSTM neural network designed above. Finally, the observation information and the maneuver intention recognition information are integrated and input into the joint maneuver intention prediction network. Among them, the observation information preprocessing process is shown in Figure 4. The observation information is shown below: \(h\) is the altitude of the HGV. \(\theta\) and \(\phi\) are the latitude and longitude of the vehicle. \(R_{e}\) is the radius of the Earth. \(v\) can be expressed as:  
\[v=\sqrt{h^{2}+\mathrm{R}_{e}^{2}\theta^{2}\mathrm{cos}^{2}\phi+\mathrm{R}_{e}^ {2}\,\phi^{2}} \tag{13}\]  
\(\gamma\) can be expressed as:  
\[\gamma=acos\left(\frac{h}{\sqrt{R_{e}^{2}\theta^{2}\mathrm{cos}^{2}\phi+R_{e}^ {2}\phi^{2}}}\right) \tag{14}\]  
The rate of the heading angle \(\phi\) can be calculated by:  
\[\psi\mathrm{=}\mathrm{acos}\left(\frac{v*\psi*\mathrm{cos}\gamma}{\sqrt{\dot{v }^{2}+v^{2}}}\right) \tag{15}\]  
Four tags \(A_{1}\), \(A_{2}\), \(A_{3}\) and \(A_{4}\) need to be recognized to determine the vehicle's maneuver intention. The input of the \(A_{1}\) recognition network includes six parameters: \(h\), \(\dot{h}\), \(v\), \(\dot{v}\), \(\gamma\) and \(\dot{\gamma}\). The output is \(A_{1}\). The \(A_{2}\) recognition network is divided into the \(QEG\) and \(SG\) maneuver parameter recognition networks. The inputs of the \(A_{2}\) recognition network are \(h\), \(h\), \(v\), \(\dot{v}\), \(\gamma\) and \(\dot{\gamma}\). The output is \(A_{2}\). The input of the \(A_{3}\) recognition network includes five parameters: \(\theta\), \(\phi\), \(\dot{\theta}\), \(\dot{\phi}\) and \(\dot{\psi}\). The output is \(A_{3}\). The input of the \(A_{4}\) recognition network is the same as the \(A_{3}\) recognition network. Then the output is \(A_{4}\). Figure 3: The joint prediction algorithm based on PSLSTM. The uncertainty of the future intention trend of the vehicle is intense, and the number of coupling relationships among the existing parameters is large. In the maneuver intention prediction stage, 15 parameters are put into the joint prediction network. The parameters include: \(h,\upsilon,\gamma,\theta,\phi,\dot{h},\upsilon,\dot{\gamma},\dot{\theta},\dot{ \phi},\dot{\psi}\), and four intention parameters, \(A_{1}\), \(A_{2}\), \(A_{3}\), \(A_{4}\). Two fully connected layers are added to the network to extract the coupling relationship between the parameters. Then, a parallel-stacked LSTM neural network is added to learn and predict the temporal association between the data. Finally, a fully connected layer is added to map the learned data into the output form of four neurons to make a sequential output. The intention prediction of HGVs has high complexity.