# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
###### Abstract  
Driver intention recognition is essential for optimizing driving decisions by dynamically adjusting speed and trajectory to enhance system performance. However, in the underground coal mine environment, traditional vision-based methods face significant limitations in accuracy and adaptability. To effectively improve the accuracy of vision-based driver intention recognition, this study introduces a novel approach leveraging cross-modal knowledge distillation (CMKD) to integrate electroencephalography (EEG) signals with video data to identify driver intentions in coal mining operations. By combining these modalities, the method capitalizes on their complementary strengths to achieve a more comprehensive understanding of driver intent. Experimental analysis across various models evaluates the performance of the proposed CMKD method, which integrates EEG signals with video data. Results reveal a substantial improvement in recognition accuracy over traditional machine vision-based approaches, with a maximum accuracy of 84.38%. This advancement enhances the reliability of driver intention detection and offers more robust support for decision making in automated mine transport systems. m m  
requiring precise localization. [PERSON] et al. [8] utilized forward video sequences to detect turn signals and hazard lights, integrating both spatial and temporal information for action prediction. [PERSON] et al. [9] presented DIPNet, a novel neural network that analyzed interior, exterior, and combined video data to forecast driver intentions seconds in advance. Despite these advancements, the unique challenges of underground mining environments hinder the effectiveness of intention recognition systems relying solely on machine vision. Poor lighting, often limited to vehicle-provided illumination [10,11], results in low-quality video images that complicate visual analysis [12,13]. Moreover, machine vision systems often fail to infer intentions such as turning, especially when images lack turning features or dynamic context. This deficiency increases the likelihood of misinterpreting driving intentions. Enhancing intention recognition accuracy in autonomous mine transport vehicles can be achieved by integrating physiological signals with vision-based methods. Recent advancements in artificial intelligence have significantly improved the use of electroencephalographic (EEG) signals to decode driver intentions [14, 15, 16]. EEG signals, which reflect brain activity, provide valuable insights into cognitive states and intentions [17,18], enabling enhanced decision-making and control systems for improved safety and accuracy. Compared with electromyogram (EMG) [19] and eye-tracking (ET) [20], EEG [21] has the advantage of directly capturing the driver's internal mental and cognitive states, such as attention, fatigue, and stress, without being affected by physical factors like muscle movements or eye fatigue. For example, [PERSON] et al. [22] proposed an expanded CNN model with a gating mechanism (GDCNN) to decode four distinct driving intentions from EEG signals. [PERSON] et al. [23] analyzed neural patterns associated with emergency and soft braking, developing an EEG-based method to distinguish these intentions. [PERSON] et al. [24] proposed an EEG-driven approach to differentiate emergency from normal braking. [PERSON] et al. [25] explored EEG signals to decode steering intentions at intersections. This makes EEG particularly suitable for improving intention recognition in complex and dynamic environments such as autonomous driving in mines. When integrated with exterior video data, it can significantly improve the accuracy and depth of driving intention recognition, addressing the limitations of methods that rely solely on video. Knowledge distillation [26] is a model compression technique that enables a smaller student model to learn from a complex teacher model by transferring soft labels.