# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
## 3 Intelligent Detection Methods for Driver Fatigue and Distraction
### Intelligent Detection Methods Based on Driver's Facial Features  
Intelligent detection methods based on drivers' facial features primarily collect facial information through cameras installed inside vehicles. After image processing, machine learning or deep learning methods are used to identify the facial region and extract feature points, followed by model training. This process enables the detection of driver fatigue or distraction states. These methods are widely recognized and applied due to their real-time performance, low cost, and non-invasive nature. The basic workflow of intelligent detection methods based on facial features is as follows, as illustrated in Figure 3. First, the Eye Aspect Ratio (EAR) and the Mouth Aspect Ratio (MAR) are calculated. Then, based on the EAR value, eye fatigue assessment indicators are computed, such as the percentage of eyelid closure over the pupil over time (PERCLOS) [32], blink frequency, and the duration of continuous eye closure. Based on the MAR value, yawning can be determined by calculating the yawn frequency and duration. Finally, by integrating these assessment indicators into a model, a comprehensive judgment is made on whether the driver is in a state of fatigue or distraction. Currently, Driver State Monitoring (DSM) systems widely applied in actual vehicles often use traditional machine learning libraries such as dlib to extract features of the eyes and mouth, calculating fatigue and distraction state assessment indicators using fixed thresholds. This method is widely adopted due to its simplicity and low sensor requirements. However, its recognition accuracy and detection effectiveness need improvement, and it overlooks individual differences among drivers. To address the challenge of low accuracy in face recognition and feature point extraction, the academic community has proposed various improvement schemes. [PERSON] et al. [33] enhanced face detection accuracy significantly by integrating an improved YOLO v3 method with the Kalman filter algorithm for face detection and using the boosting tree algorithm to extract facial feature points, effectively solving the problem of missed detections caused by glasses and hats. Although this method has made progress in accuracy, its complexity and computational cost are higher. [PERSON] et al. [34] pointed out the correlation between drivers' eye position and ethnicity, greatly improving positioning accuracy through a skin color recognition model and a bidirectional integral projection method, but their accuracy may be limited to specific groups. [PERSON] et al. [35] combined 3D Convolutional Neural Networks (3D-CNN) and Bidirectional Long Short-Term Memory networks (Bi-LSTM) to propose the 3D-LTS network, which effectively recognizes subtle facial movements, improving the accuracy of mouth feature point extraction and yawn detection, as shown in Figure 4. Although this method performs excellently  
Figure 2: Classification of intelligent detection techniques for driver fatigue and distraction. in dynamic feature recognition, it still faces challenges in real-time performance and resource consumption. To address the shortcomings of traditional methods that do not consider individual differences among drivers, some studies have optimized detection approaches. [PERSON] et al. [36] improved detection effectiveness by constructing a driver identity information database and extracting facial features as a reference for assessing the driver's state. This personalized method has obvious advantages, but its application range may be limited by the size of the offline library. [PERSON] et al. [37] used a Deep Cascaded Convolutional Neural Network (DCCNN) to detect facial features and established an SVM model based on facial features, replacing traditional uniform threshold methods and adding personalized elements to fatigue and distraction detection. Although this increases the personalization of detection, the model's general applicability and accuracy still need further verification, as shown in Figure 5. [PERSON] et al.