# Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles
## 1 Introduction  
According to the National Highway Traffic Safety Administration [1] report, 11.2% of road traffic accidents in the United States in 2015 involved at least one large freight vehicle. In developing countries such as India, the situation is even worse. The National Crime Bureau of India (NCRB) [2] records that in 2015, commercial freight vehicles accounted for 19.4% of the total fatalities in traffic accidents. At the same time, with the acceleration of globalization, the demand for road freight transportation will further increase, and drivers often sacrifice sleep and rest time to drive for a long time under the pressure of life. Fatigue driving is one of the main causes of traffic accidents of freight vehicles [3]. The fatigue state of freight vehicle drivers has unique characteristics due to their occupational characteristics. In 2013, the US Centers for Disease Control and Prevention [4] stated that commercial freight drivers were more likely to be drowsy or fatigued than other drivers. Through questionnaires, [PERSON], [PERSON] [5] found that the driving experience of long-distance freight drivers and long-distance bus drivers only partially overlaid. Compared with bus drivers, most freight drivers said that they often felt fatigue, while relatively few bus drivers said that fatigue was the main problem of their driving. [PERSON], [PERSON] et al. [6] found that compared to not providing feedback to drivers when fatigue was detected, providing feedback to the company and the cockpit in real time could effectively reduce the occurrence of fatigue driving events. In summary, it is of great significance to quickly and accurately detect the fatigue driving behavior of freight vehicles. At present, the research on freight vehicle fatigue driving mainly focuses on the correlation between driver fatigue and macro factors such as daily work and rest, workpressure, management system, physical quality, etc. [7; 8; 9]. [PERSON], [PERSON] et al. [10] studied the correlation between freight driver fatigue, work organization, accident experience, etc. The results showed that driver fatigue was significantly correlated with rest, transportation route arrangement, and management system. [PERSON], [PERSON] et al. [11] invited professional freight drivers to participate in a long-term natural driving study to explore the relationship between the body mass index of commercial freight drivers and fatigue driving events, and the results showed that obese individuals were more likely to be fatigued. However, there are few studies on the detection of fatigue degrees of freight vehicle drivers. [PERSON], [PERSON] et al. [12] carried out 2 h, 3 h, and 4 h natural driving tests of commercial drivers to test the correlation between drivers' visual behaviors and subjective fatigue degree. According to sensor data source of the fatigue detection system, the existing fatigue driving behavior detection systems are mainly divided into two types: invasive and non-invasive [13; 14]. The invasive fatigue detection system collects physiological data such as electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG), heart rate detection, and other physiological data of the driver; analyzes the physiological characteristics of the driving process; and realizes the detection of the driver's fatigue degrees [15; 16; 17]. However, the intrusive fatigue driving detection requires contact with the driver's body, which may easily lead to distraction or discomfort for the driver; therefore, its application in real-life situations is restricted. The non-intrusive systems primarily detect the driver's fatigue degrees by extracting the driver's facial features, vehicle operating data, and other information. During the detection process, the driver's normal driving will not be disturbed, and the data acquisition and fatigue detection can objectively reflect the driver's actual operating behavior or states [18; 19].