# 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  
Twelve classification methods were evaluated for EEG feature classification, including CNN [41], long short-term memory networks (LSTMs) [42], transformers [43], and temporal convolutional networks (TCNs) [44]. CNNs extract features automatically from input data using stacked layers, while LSTM captures long-term dependencies through memory cells and gating mechanisms. Transformers, leveraging an attention-based architecture, excel in sequence-to-sequence tasks. TCNs address the limitations of traditional recurrent neural networks by utilizing convolutional layers and residual connections to effectively model long-term dependencies and patterns in time-series data. For EEG feature classification, 20% of the dataset was randomly selected as the test set. From the remaining data, 10% was designated as the validation set, with the remainder used for training, yielding a ratio of 2919:325:812. The data underwent normalization. Using this approach, five data splits were randomly generated for training and testing. The model was trained for 500 epochs using the cross-entropy loss function, and parameters were optimized with the Adam algorithm [45] at a learning rate of 3e-4, which was reduced by a factor of 0.1 after 450 epochs. The batch size was set to 8. Metrics for evaluation included average precision (Avg. Precision), average recall (Avg. Recall), average F1 score (Avg. F1) per category, and average accuracy (Avg. Accuracy). The average evaluation results across five runs on the test set are shown in Table 3. \begin{table}
\begin{tabular}{c 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{3}{*}{CNN} & left & 66.97 & 69.84 & 68.34 & \multirow{3}{*}{\(69.28\pm 0.62\)} \\  & right & 72.22 & 64.57 & 68.17 & \\  & normal & 74.24 & 69.18 & 71.60 & \\  & avoidance & 64.91 & 73.73 & 69.02 & \\ \hline \multirow{3}{*}{LSTM} & left & 63.17 & 68.80 & 65.81 & \multirow{3}{*}{\(67.28\pm 0.53\)} \\  & right & 71.23 & 64.10 & 67.42 & \\  & normal & 70.78 & 71.79 & 71.27 & \\  & avoidance & 66.73 & 66.87 & 66.79 & \\ \hline \multirow{3}{*}{CNN\_LSTM} & left & 56.87 & 60.73 & 58.73 & \multirow{3}{*}{\(69.38\pm 1.55\)} \\  & right & 60.12 & 52.86 & 56.48 & \\  & normal & 63.21 & 61.74 & 62.43 & \\  & avoidance & 57.21 & 62.45 & 59.75 & \\ \hline \multirow{3}{*}{CNN\_BiGRU} & left & 58.34 & 58.95 & 58.62 & \multirow{3}{*}{\(56.75\pm 1.36\)} \\  & right & 59.65 & 59.14 & 59.37 & \\ \cline{1-1}  & normal & 63.10 & 60.58 & 61.81 & \\ \cline{1-1}  & avoidance & 59.40 & 61.57 & 60.45 & \\ \hline \multirow{3}{*}{Transformer} & left & 63.51 & 67.44 & 65.39 & \multirow{3}{*}{\(59.09\pm 2.28\)} \\  & right & 67.34 & 66.10 & 66.66 & \\ \cline{1-1}  & normal & 72.43 & 69.56 & 70.87 & \\ \cline{1-1}  & avoidance & 68.55 & 68.24 & 68.33 & \\ \hline \multirow{3}{*}{CNN\_Attention} & left & 53.01 & 55.39 & 54.16 & \multirow{3}{*}{\(67.49\pm 0.68\)} \\  & right & 58.56 & 54.86 & 56.63 & \\ \cline{1-1}  & normal & 59.62 & 59.23 & 59.40 & \\ \cline{1-1}  & avoidance & 55.83 & 57.45 & 56.60 & \\ \hline \multirow{3}{*}{CNN\_Transformer} & left & 56.20 & 58.32 & 57.11 & \multirow{3}{*}{\(59.09\pm 2.28\)} \\  & right & 60.66 & 55.53 & 57.95 & \\ \cline{1-1}  & normal & 63.46 & 58.36 & 60.63 & \\ \cline{1-1}  & avoidance & 57.04 & 64.22 & 60.39 & \\ \hline \multirow{3}{*}{LSTM\_Attention} & left & 61.95 & 66.39 & 64.09 & \multirow{3}{*}{\(67.49\pm 0.68\)} \\  & right & 72.46 & 64.29 & 68.12 & \\ \cline{1-1}  & normal & 70.37 & 70.12 & 70.54 & \\ \cline{1-1}  & avoidance & 65.66 & 68.53 & 67.06 & \\ \hline \multirow{3}{*}{CNN\_TCN} & left & 63.05 & 63.35 & 63.09 & \multirow{3}{*}{\(65.71\pm 0.67\)} \\  & right & 67.69 & 65.53 & 66.42 & \\ \cline{1-1}  & normal & 67.92 & 68.79 & 68.23 & \\ \cline{1-1}  & avoidance & 64.81 & 65.00 & 64.86 & \\ \hline \hline \end{tabular}
\end{table}