# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 3 Result and Discussion
### Driving Intention Recognition Analysis Based on EEG Features
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Classes** & \
\begin{tabular}{c} **Avg. Precision** \\ **(\%)** \\ \end{tabular} & **Avg. Recall (\%)** & **Avg. F1 (\%)** &
\begin{tabular}{c} **Avg. Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{TCN\_Attention} & left & 69.86 & 70.37 & 70.11 & \multirow{4}{*}{\(71.55\pm 0.51\)} \\  & right & 72.55 & 68.95 & 70.68 & \\  & normal & 73.18 & 76.91 & 74.94 & \\  & avoidance & 70.75 & 69.90 & 70.29 & \\ \hline \multirow{4}{*}{TCN\_Attention} & left & 61.51 & 63.87 & 62.66 & \multirow{4}{*}{\(64.78\pm 1.02\)} \\  & right & 67.03 & 63.43 & 65.05 & \\ \cline{1-1}  & normal & 68.40 & 68.50 & 68.44 & \\ \cline{1-1}  & avoidance & 62.71 & 63.24 & 62.81 & \\ \hline \multirow{4}{*}{TCN\_LSTM} & left & 57.38 & 59.68 & 58.46 & \multirow{4}{*}{\(59.61\pm 0.57\)} \\  & right & 61.23 & 57.52 & 59.25 & \\ \cline{1-1}  & normal & 62.03 & 61.74 & 61.84 & \\ \cline{1-1}  & avoidance & 58.05 & 59.51 & 58.76 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._For the four-class classification of driving intentions based on EEG features stimulated by video data, the TCN_CNN model achieves the highest average classification accuracy at 67.31%. Given these findings, the TCN_CNN model, demonstrating the best performance, is selected as the teacher model for training the subsequent CMKD model. The TCN_CNN architecture diagram is shown in Figure 8.  
The TCN_CNN adopts a parallel processing approach where EEG features are extracted and processed separately, and then, the results are fused to better identify significant features for the classification of driving intentions. The input EEG feature size is, and it is processed through both TCN and CNN for feature extraction. In the TCN module, EEG features are extracted using 1D convolution, and the feature sequence length is adjusted by a cropping module. The features then undergo normalization via the ReLU activation function and Dropout layer, with residual connections enhancing the network's expressive power. The specific structure is shown in Figure 9a. After three layers of stacked TCN processing, the output EEG feature size becomes [8, 128, 288]. In the CNN module, EEG features are first processed by 1D convolutional layers for local feature extraction followed by ReLU activation for nonlinear transformation. Max pooling operations are then applied for downsampling, further reducing the feature dimensions and extracting more prominent features. The specific structure is shown in Figure 9b. After three layers of stacked CNN processing, the output EEG feature size becomes [8, 128, 36]. Finally, the EEG features extracted by the CNN module are fused with the output from the TCN module to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Classes** & \
\begin{tabular}{c} **Avg. Precision** \\ **(\%)** \\ \end{tabular} & **Avg. Recall (\%)** & **Avg. F1 (\%)** &
\begin{tabular}{c} **Avg. Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline TCN\_Attention & left & 56.96 & 59.52 & 58.12 \\  & right & 51.59 & 52.69 & 52.04 \\  & normal & 58.30 & 59.62 & 58.94 \\  & avoidance & 56.89 & 51.63 & 54.08 \\ \hline TCN\_LSTM & left & 49.52 & 54.42 & 51.85 \\  & right & 48.66 & 50.67 & 49.62 \\  & normal & 61.91 & 54.71 & 58.07 \\  & avoidance & 53.05 & 51.63 & 52.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)  
Figure 8: TCN_CNN model architecture diagram. integrate information from different modules. The fused features are flattened through a data flattening layer before being input into the fully connected layer for further processing. In the fully connected layer, features undergo a nonlinear transformation via the ReLU activation function and are subsequently regularized by the Dropout layer. Through this processing pipeline, the model outputs the corresponding driving intention category.