# 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  
Based on previous experiments and analysis, we selected the TCN_CNN model trained on EEG features as the teacher model and the LRCN_18 model, trained on video data, as the student model. The TCN_CNN model combines the strengths of TCN and CNN, effectively extracting multi-level information features from the extracted EEG features and better capturing the complex patterns within the EEG features. Specifically, TCN captures long-range dependencies during feature extraction, while CNN helps to extract local features. The combination of both makes TCN_CNN highly effective in processing EEG features. On the other hand, the LRCN_18 model extracts efficient spatial features from video frames and fully utilizes the temporal information between video frames to capture dynamic changes in the video data. LRCN_18 emphasizes the integration of spatial and temporal information, enabling it to better process video data and play an important role in driving intention recognition tasks. Through the CMKD framework, the knowledge extracted by the teacher model (TCN_CNN) from EEG features can be effectively transferred to the student model (LRCN_18). This allows the student model to leverage the information features embedded in EEG features, improving its recognition accuracy on video data. As both LRCN_18 and TCN_CNN excel in their respective domains, their synergistic effect within the CMKD framework helps the student model more accurately extract and recognize driving intentions from video data. The model parameters are selected by setting the temperature \(T\) within the range and the imitation factor \(\lambda\) within [0.25, 0.5, 0.75, 1]. The student model's parameters are consistent with those outlined in Section 3.1. After training, the performance of each optimal model from the five-fold cross-validation is evaluated on the test set with metrics  
Figure 9: Temporal Block model and CONV model architecture diagram: (**a**) Temporal Block model; (**b**) CONV model. including average accuracy (Avg.) and maximum accuracy (Max) shown in Table 5. The sensitivity analysis curves for parameters \(\lambda\) and \(T\) are shown in Figures 10 and 11. From Figure 10, it can be seen that when \(\lambda=0.5\) and \(\lambda=0.75\), the model performs better when \(T=100\), achieving the optimal accuracy. However, when \(\lambda=1\), the model reaches the optimal accuracy when \(T=20\). Similarly, from Figure 11, it can be observed that when \(T=20\) and \(\lambda=1\), the model achieves the best performance. Based on the above analysis, it can be concluded that when \(\lambda=1\) and \(T=20\), the algorithm achieves an average accuracy of \(75.63\%\) and a maximum accuracy of \(84.38\%\). This significant performance enhancement demonstrates the effectiveness of the proposed method in recognizing driving intentions. To validate the effectiveness of the proposed method more comprehensively, we performed the Wilcoxon signed-rank test [46] on the results from the five-fold cross-validation of both methods to assess the overall significance difference between our method and LRCN_18. The results show that the proposed algorithm significantly outperforms the  
Figure 11: The sensitivity analysis curve for parameter \(T\). Figure 10: The sensitivity analysis curve for parameter \(\lambda\).