# Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles
## 1 Introduction
Therefore, at present, the non-intrusive methods have become more popular in the field of driver fatigue detection, and most of them obtain data through simulated driving or real vehicle experiments. From the perspective of fatigue driving detection in a simulated driving environment, [PERSON], [PERSON] et al. [20] proposed a detection algorithm based on multi-index fusion and state recognition network. In this study, a multi-task level convolutional neural network was used to detect faces and feature points, and a fatigue detection model was established by combining the two features of eyes and mouth states. [PERSON], [PERSON] et al. [21] input different combinations of vehicle operating parameters into the fatigue driving detection model based on the random forest algorithm, and the research results show that the fatigue degrees of drivers can be detected according to the three indexes of steering angle, lateral acceleration, and longitudinal acceleration. [PERSON], [PERSON] et al. [22] took the time dependence of the transition between sleepy and awake states into consideration, and designed the dynamic Bayesian network fatigue detection algorithm with the steering angle, pedal, vehicle speed, and acceleration as inputs, which improved the detection accuracy. From the perspective of experimental fatigue driving detection of real vehicles, [PERSON], [PERSON] et al. [23] detected the fatigue degree of drivers based on different steering angles and yaw angles in the real driving environment, and the detection accuracy reached 88.02%. [PERSON], [PERSON] et al. [24] assuming that vehicle acceleration, vehicle rotation mode, driver's head position, and head rotation are fatigue related indicators, and developed a fatigue driving detection system. The above research results show that the fatigue degree of the driver can be detected by using the parameters of the vehicle such as steering angle, lateral position, heading angle, and acceleration, and the detection accuracy of the model will be improved based on multiple parameters. However, most of the above research data are from driving simulator environments or real vehicle experiments. On the one hand, the reliability of a fatigue monitoring system developed on this basis still lacks effective verification under real vehicle conditions, and on the other hand, the number of subjects is small. From the point of the principle of the detection method, the vast majority of current fatigue driving detection studies have used the machine learning and deep learning algorithms, including cascade regression tree, decision tree, neural network, support vector machine (SVM), random forests, Bayesian networks, etc., and combine the collected data of face, physiological, and vehicle operation to detect fatigue driving [25; 26; 27; 28]. However, first of all, it is difficult to calibrate some parameters accurately. For example, in the sample training process, some basic parameters and training function parameters are randomly generated, the training efficiency is low, and the influence of the number of training samples and other parameters on the model detection accuracy and model stability is seldom considered. Secondly, the intermediate learning process cannot be observed and the output results are difficult to explain, which will affect the credibility and acceptability of the results. Finally, the requirements for data quality and content are relatively high, and the practical application is more difficult. With the extensive application of various vehicle operation management systems, vehicle operation data and drivers' facial monitoring video on the actual road can be acquired in real time and stored for a long time, which greatly improves the richness and objectivity of data sources [29]. At the same time, the \(k\)-nearest neighbor algorithm is the most intuitive in principle and the method is simple. It depends on the number of surrounding training samples, and the established model has strong stability [30].