# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 3 Result and Discussion
### Driving Intention Recognition Analysis Based on Cross-Modal Knowledge Distillation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Parameter** \\ **Combinations (\(\lambda\)-T)** \\ \end{tabular} & **Classes** & \
\begin{tabular}{c} **Avg. Precision** \\ **(\%)** \\ \end{tabular} & **Avg. Recall (\%)** & **Avg. F1 (\%)** &
\begin{tabular}{c} **Avg. Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{0.75-100} & left & 76.29 & 62.50 & 67.95 & \multirow{3}{*}{71.25 \(\pm\) 6.78} \\  & right & 73.31 & 65.00 & 64.57 & \\  & normal & 67.28 & 100.00 & 79.49 & \\  & avoidance & 82.79 & 57.50 & 66.48 & \\ \hline \multirow{3}{*}{1-20} & left & 91.56 & 62.50 & 72.21 & \multirow{3}{*}{75.63 \(\pm\) 5.59} \\  & right & 76.64 & 77.50 & 72.14 & \\ \cline{1-1}  & normal & 68.27 & 100.00 & 80.44 & \\ \cline{1-1}  & avoidance & 95.00 & 62.50 & 73.82 & \\ \hline \multirow{3}{*}{1-50} & left & 87.79 & 62.50 & 71.19 & \multirow{3}{*}{73.12 \(\pm\) 1.71} \\  & right & 79.01 & 62.50 & 68.07 & \\ \cline{1-1}  & normal & 61.64 & 100.00 & 75.68 & \\ \cline{1-1}  & avoidance & 88.81 & 67.50 & 75.76 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results for the optimal parameter combinations. Figure 12: The row-normalization confusion matrix of the model with the highest recognition accuracy: (**a**) LRCN_18; (**b**) parameter combinations (1–20). assess the impact of different parameter configurations, we present bar charts comparing average and maximum accuracy across various settings in Figure 13.  
When \(\lambda\geq 0.5\) and \(T>10\), the algorithm consistently surpasses the LRCN_18 method, which relies exclusively on video data for training. This improvement stems from the loss function placing greater emphasis on knowledge transfer from the teacher model when \(\lambda\geq 0.5\). As \(\lambda\) increases, the student model more effectively leverages the teacher model's output, gaining critical insights into driving intentions. This transfer mechanism enables the student model to assimilate richer and more targeted knowledge during training, significantly enhancing its performance, reliability, and accuracy in driving intention recognition tasks. Accurately interpreting driver intentions in complex coal mine environments is crucial for enhancing transportation efficiency, safety, and reliability while reducing accident risks. The method proposed in this paper implicitly incorporates EEG data features into driving intention recognition based on video data. In practical applications, high-precision intention recognition can be achieved using only video cameras. This method has significant engineering application value in real-time decision making for autonomous driving in underground mines. Additionally, the model has low computational complexity and small latency, effectively addressing the issue of recognizing the driving intention of mining transport vehicles, thus laying a theoretical foundation for breakthroughs in autonomous driving in underground mines. Regarding the age and number of drivers, the paper selects drivers aged 20-25, with a sample size of 13. For the underground coal mine scenario, since all workers are male and require coal mine driving experience and specific backgrounds, this dataset is somewhat representative and generalizable, but it still has limitations. This study considers drivers of a specific age group only, excluding other age groups, which may not reflect the diverse needs of different drivers. This study currently focuses on intention recognition in simple underground coal mine scenarios and serves as a preliminary exploration of the method. In future research, we will expand the sample size to include more intention recognition scenarios during driving, such as reversing, and conduct studies and analyses on drivers from different age groups, discussing the impact of varying numbers of participants on model accuracy, to further enhance the model's generalizability and robustness. Figure 13: Performance of CMKD for driving intention recognition: (**a**) average accuracy; (**b**) maximum accuracy.