Long tunnel group driving fatigue detection model based on XGBoost algorithm

Abstract

Driving fatigue is one of the important causes of accidents in tunnel (group) sections. In this paper, in order to effectively identify the driving fatigue of tunnel (group) drivers, an eye tracker and other instruments were used to conduct real vehicle tests on long tunnel (group) expressways and thus obtain the eye movement, driving duration, and Karolinska sleepiness scale (KSS) data of 30 drivers. The impacts of the tunnel and non-tunnel sections on drivers were compared, and the relationship between blink indexes, such as the blink frequency, blink duration, mean value of blink duration, driving duration, and driving fatigue, was studied. A paired t-test and a Spearman correlation test were performed to select the indexes that can effectively characterize the tunnel driving fatigue. A driving fatigue detection model was then developed based on the XGBoost algorithm. The obtained results show that the blink frequency, total blink duration, and mean value of blink duration gradually increase with the deepening of driving fatigue, and the mean value of blink duration is the most sensitive in the tunnel environment. In addition, a significant correlation exists between the driving duration index and driving fatigue, which can provide a reference for improving the tunnel safety. Using the mean value of blink duration and driving duration as the characteristic indexes, the accuracy of the driving fatigue detection model based on the XGBoost algorithm reaches 98%. The cumulative and continuous tunnel proportion effectively estimates the driving fatigue state in a long tunnel (group) environment.