# 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 c c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{\(\lambda=0.25\)} & \multicolumn{2}{c}{\(\lambda=0.5\)} & \multicolumn{2}{c}{\(\lambda=0.75\)} & \multicolumn{2}{c}{\(\lambda=1\)} \\ \hline \(T\) & **Avg. (\%)** & **Max (\%)** & **Avg. (\%)** & **Max (\%)** & **Avg. (\%)** & **Max (\%)** & **Avg. (\%)** & **Max (\%)** \\ \hline
1 & \(59.38\pm 5.41\) & 68.75 & \(51.25\pm 10.73\) & 68.75 & \(56.87\pm 8.67\) & 71.88 & \(67.50\pm 5.68\) & 75.00 \\
2 & \(58.13\pm 4.19\) & 62.50 & \(64.37\pm 1.71\) & 65.62 & \(58.13\pm 3.57\) & 62.50 & \(49.38\pm 6.78\) & 59.38 \\
5 & \(58.13\pm 4.74\) & 65.62 & \(55.00\pm 10.03\) & 71.88 & \(59.38\pm 6.25\) & 68.75 & \(60.00\pm 8.95\) & 71.88 \\
10 & \(55.62\pm 6.01\) & 65.62 & \(67.50\pm 2.80\) & 71.88 & \(67.50\pm 4.74\) & 71.88 & \(57.50\pm 8.73\) & 71.88 \\
20 & \(53.75\pm 10.46\) & 71.88 & \(63.62\pm 5.41\) & 75.00 & \(60.63\pm 10.74\) & 75.00 & \(75.63\pm 5.59\) & 84.38 \\
50 & \(49.38\pm 11.69\) & 68.75 & \(65.62\pm 5.85\) & 75.00 & \(61.87\pm 9.48\) & 78.12 & \(73.12\pm 1.71\) & 75.00 \\
100 & \(56.25\pm 7.97\) & 68.75 & \(67.50\pm 6.09\) & 78.12 & \(71.25\pm 6.78\) & 81.25 & \(67.50\pm 9.27\) & 78.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results for different parameter combinations. baseline algorithm (\(p=0.0312<0.05\)), further demonstrating that our method performs significantly better than the baseline model. To further assess its effectiveness, we selected the optimal parameter combination and averaged the evaluation metrics--average precision, recall, F1 score, and accuracy--based on five-fold cross-validation results on the test set in Table 6.  
The 1-20 parameter combination delivers the best performance, excelling in both average precision and F1 score, particularly achieving an impressive 80.44% average F1 for the \"normal\" class. Compared with other parameter combinations and the LRCN_18 model trained solely on video data, the 1-20 configuration reduces false positives while ensuring comprehensive class identification, maintaining both high accuracy and class coverage. This demonstrates the superior balance and stability of the method across multiple metrics, making it the most effective and reliable choice for multi-class classification tasks. The row-normalization confusion matrix with the highest recognition accuracy of LRCN_18 and parameter combinations (1-20) is shown in Figure 12.  
As shown in Figure 12, our method outperforms the LRCN_18 in terms of prediction accuracy for the four driving intentions. Among them, the prediction accuracies for right and normal driving intentions are the highest, with no misclassifications. It can be observed that the CMKD method significantly improves the recognition of right and normal driving intentions. However, recognizing left and avoidance driving intentions is more challenging, possibly due to confusion between these two categories, which caused misclassifications. Further optimization for these two intentions will be needed in the future. To visually