# 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 Behavioral Actions  
During the driving process, the actions and behaviors of drivers can significantly reflect their attention states. Behaviors such as making phone calls, texting, smoking, or reaching for items can lead to driver distraction. Accordingly, monitoring these behaviors can effectively determine whether a driver is distracted. [PERSON] et al. [47] utilized a deep convolutional neural network (CNN) to design a system capable of recognizing a variety of driving behaviors, including normal driving, looking at rearview mirrors in various directions, using the car radio, texting, and making phone calls.  
However, given the small differences between normal and distracted driving behaviors and the high similarity of some actions, relying solely on a CNN might lead to recognition errors. Therefore, [PERSON] et al. [48] introduced a bilinear fusion network and attention mechanism, proposing a convolutional neural network specifically designed for driving behavior recognition, BACNN, which showed a good performance on the State Farm dataset. To further improve recognition accuracy and real-time performance, [PERSON] et al. [49] developed a specialized intertwined deep convolutional neural network, InterCNN. [PERSON] et al. [50] established an expert knowledge rule system and a discrete dynamic Bayesian classification model to predict the level of distraction in video frame sequences, providing a quantitative assessment of driver behavior.  
In addition to using cameras to recognize driver actions and behaviors through visual images, some researchers have used wearable devices to collect signals reflecting driver behavior. However, large wearable devices are not portable and are costly, making them suitable only for experimental data collection and difficult to use for real-time detection. Thus, researching and developing portable wearable devices for the real-time monitoring of driver states is a trend. [PERSON] et al. [51] proposed a method to identify driver distraction using a wristband equipped with an Inertial Measurement Unit (IMU), i.e., collecting acceleration data from the driver's right wristband's IMU and building a Convolutional Long Short-Term Memory (ConvLSTM) deep neural network model for training. The results showed that ConvLSTM achieved a better detection accuracy than CNN and LSTM models. [PERSON] et al. [52] specifically targeted dangerous behaviors such as using phones, eating, and smoking by drivers. They proposed a deep-learning-based classification detection method by capturing images with left and right infrared cameras.  
In summary, these methods offer high real-time capabilities. Once they detect dangerous driving behaviors that could distract drivers, immediate warnings can be issued without the need for cumulative time to continuously observe changes in driver characteristics. Although these technologies have shown efficiency in the real-time monitoring of driver states and can promptly identify and warn of potential distractions, they still face challenges such as scene limitations and individual behavior differences. Future research needs to continue exploring more accurate and personalized recognition methods to adapt to the diversity and complexity of driver behaviors. Additionally, developing more convenient, lower-cost tactile motion sensing systems for broader applications is an important research direction.