# 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

Table 3: Classification results of EEG features for all video stimuli. The TCN_CNN model demonstrates superior performance in classifying driving intention EEG features, achieving an average accuracy of 71.55%. This high performance is attributed to the model's ability to integrate the TCN and CNN architectures, enabling the detection of subtle feature variations. For further analysis, EEG features from all subjects were partitioned into training, validation, and test sets in a 2600:624:832 ratio, following the data partitioning methodology applied in video-based driving intention recognition. The data was normalized. The average evaluation metrics for each optimal model on the test set after training are detailed in Table 4. \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{4}{*}{CNN} & left & 72.37 & 64.04 & 67.84 & \multirow{4}{*}{\(71.55\pm 0.51\)} \\  & right & 65.01 & 60.58 & 62.68 & \\  & normal & 63.40 & 71.15 & 66.97 & \\  & avoidance & 60.11 & 63.27 & 61.60 & \\ \hline \multirow{4}{*}{LSTM} & left & 69.94 & 66.73 & 68.17 & \multirow{4}{*}{\(66.08\pm 0.60\)} \\  & right & 69.65 & 63.08 & 65.66 & \\  & normal & 67.49 & 69.04 & 68.07 & \\  & avoidance & 59.95 & 65.48 & 62.54 & \\ \hline \multirow{4}{*}{CNN\_LSTM} & left & 46.72 & 49.90 & 48.25 & \multirow{4}{*}{\(49.06\pm 2.24\)} \\  & right & 44.59 & 43.17 & 43.87 & \\ \cline{1-1}  & normal & 56.08 & 52.60 & 54.24 & \\ \cline{1-1}  & avoidance & 49.41 & 50.30 & 49.95 & \\ \hline \multirow{4}{*}{CNN\_BiGRU} & left & 52.84 & 54.14 & 53.39 & \multirow{4}{*}{\(51.75\pm 1.72\)} \\  & right & 48.14 & 49.13 & 48.59 & \\ \cline{1-1}  & normal & 55.58 & 55.96 & 55.73 & \\ \cline{1-1}  & avoidance & 50.63 & 47.79 & 49.01 & \\ \hline \multirow{4}{*}{Transformer} & left & 68.55 & 68.46 & 68.48 & \multirow{4}{*}{\(66.64\pm 1.12\)} \\  & right & 71.57 & 59.71 & 64.79 & \\ \cline{1-1}  & normal & 66.27 & 70.96 & 68.51 & \\ \cline{1-1}  & avoidance & 62.39 & 67.40 & 64.56 & \\ \hline \multirow{4}{*}{CNN\_Attention} & left & 46.76 & 52.02 & 49.22 & \multirow{4}{*}{\(49.11\pm 2.27\)} \\  & right & 44.49 & 46.06 & 45.24 & \\ \cline{1-1}  & normal & 56.33 & 52.79 & 54.47 & \\ \cline{1-1}  & avoidance & 50.27 & 45.58 & 47.68 & \\ \hline \multirow{4}{*}{CNN\_Transformer} & left & 50.20 & 55.48 & 52.56 & \multirow{4}{*}{\(51.28\pm 3.06\)} \\  & right & 48.60 & 47.40 & 47.92 & \\ \cline{1-1}  & normal & 54.36 & 54.23 & 54.25 & \\ \cline{1-1}  & avoidance & 52.41 & 47.98 & 50.01 & \\ \hline \multirow{4}{*}{LSTM\_Attention} & left & 61.66 & 63.37 & 62.47 & \multirow{4}{*}{\(61.59\pm 0.97\)} \\  & right & 57.78 & 58.56 & 58.16 & \\ \cline{1-1}  & normal & 64.93 & 67.60 & 66.22 & \\ \cline{1-1}  & avoidance & 61.96 & 56.83 & 59.28 & \\ \hline \multirow{4}{*}{CNN\_TCN} & left & 59.15 & 61.44 & 60.19 & \multirow{4}{*}{\(58.70\pm 0.72\)} \\  & right & 54.96 & 54.04 & 54.47 & \\ \cline{1-1}  & normal & 63.88 & 63.17 & 63.43 & \\ \cline{1-1}  & avoidance & 56.98 & 56.16 & 56.1 & \\ \hline \multirow{4}{*}{TCN\_CNN} & left & 72.62 & 69.71 & 71.10 & \multirow{4}{*}{\(59.61\pm 0.57\)} \\  & right & 73.53 & 60.00 & 66.02 & \\ \cline{1-1}  & normal & 65.03 & 69.52 & 67.04 & \\ \cline{1-1}  & avoidance & 60.97 & 70.00 & 65.08 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results of EEG features based on the video data partitioning method.