# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 3 Result and Discussion
### Driving Intention Recognition Analysis Based on Video Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Classes**} & **Avg. Precision (\%)** & **Avg. Recall (\%)** & **Avg. F1 (\%)** & **Avg. Accuracy (\%)** & **Model File Size (MB)** \\ \hline \multirow{4}{*}{R3D} & left & 74.29 & 25.00 & 33.13 & \multirow{4}{*}{45.63 \(\pm\) 5.23} & \multirow{4}{*}{129.753} \\  & right & 76.91 & 35.00 & 40.22 & & \\  & normal & 39.65 & 95.00 & 54.40 & & \\  & avoidance & 78.57 & 27.50 & 38.67 & & \\ \hline \multirow{4}{*}{R(2+1)D} & left & 79.33 & 27.50 & 38.93 & \multirow{4}{*}{53.13 \(\pm\) 4.94} & \multirow{4}{*}{129.862} \\  & right & 63.79 & 52.50 & 56.18 & & \\  & normal & 70.70 & 57.50 & 57.71 & & \\  & avoidance & 45.92 & 75.00 & 51.71 & & \\  & left & 61.24 & 40.00 & 45.70 & & \\  & right & 59.09 & 45.00 & 47.34 & & \\  & normal & 56.25 & 95.00 & 68.26 & & \\  & avoidance & 94.00 & 57.50 & 66.99 & & \\ \hline \multirow{4}{*}{LRCN\_34} & left & 23.33 & 20.00 & 21.43 & \multirow{4}{*}{47.50 \(\pm\) 5.13} & \multirow{4}{*}{96.646} \\  & right & 45.67 & 47.50 & 44.17 & & \\ \cline{1-1}  & normal & 55.12 & 75.00 & 59.83 & & \\ \cline{1-1}  & avoidance & 74.17 & 47.50 & 46.52 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results for video data. Table 2 indicates that LRCN_18 achieves an average accuracy of 59.38% with a standard deviation of 3.83%. It excels in the \"normal\" category with a high average recall and average precision in the \"avoidance\" category, highlighting its reliability and stability in identifying driving intentions in complex coal mine scenarios. Meanwhile, the LRCN_18 model file size is only 57.097 MB, making it convenient for system deployment. Therefore, LRCN_18 is selected as the student model for training in the subsequent CMKD process. The structure diagram of the LRCN_18 model is shown in Figure 7.  
In the LRCN_18 model, the CNN component uses the ResNet18 architecture to extract spatial features from each frame of the image. These spatial features are then input as a temporal sequence into a bidirectional long short-term memory network (BiLSTM) to capture the temporal dependencies between the features. The BiLSTM network learns information from both directions of the sequence, updating the hidden state at each time step by utilizing both preceding and succeeding contextual information. Ultimately, based on the hidden state of the last time step, a fully connected layer is applied for transformation and classification, enabling the recognition of driving intentions. Driving intentions are typically decisions made based on the final objective of the entire sequence, making the hidden state of the last time step more representative. BiLSTM gradually integrates historical temporal information into the final hidden state, thus capturing the complete contextual information, which is particularly crucial in decision-making processes involving dependencies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Classes** & \begin{tabular}{c} **Avg.** \\ **Precision (\%)** \\ \end{tabular} & \begin{tabular}{c} **Avg. Recall** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Avg. F1 (\%)** \\ \end{tabular} & \begin{tabular}{c} **Avg. F1 (\%)** \\ \end{tabular} & \begin{tabular}{c} **Avg. F1 (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Avg.** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Model File** \\ **Size (MB)** \\ \end{tabular} \\ \hline \multirow{3}{*}{SlowFast\_32} & left & 45.33 & 25.00 & 31.74 & \multirow{3}{*}{\(57.50\pm 5.23\)} & \multirow{3}{*}{84.450} \\  & right & 50.16 & 47.50 & 47.14 & \\ \cline{1-1}  & normal & 66.62 & 77.50 & 68.97 & \\ \cline{1-1}  & avoidance & 73.33 & 80.00 & 70.95 & \\ \hline \multirow{3}{*}{SlowFast\_50} & left & 13.33 & 7.50 & 9.52 & \multirow{3}{*}{46.25\(\pm\) 8.67} & \multirow{3}{*}{131.569} \\  & right & 43.89 & 35.00 & 37.75 & \\ \cline{1-1}  & normal & 54.71 & 80.00 & 61.80 & \\ \cline{1-1}  & avoidance & 57.06 & 62.50 & 54.85 & \\ \hline \hline \end{tabular}
\end{table}