Abstract

Info

Author(s):: LI Xue1; 2; LIU Xiaoxia2; GONG Piqiang1; 2; LIN Dongmei3; CHEN Fuming2; 1. School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China; 2. The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, China; 3. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Keywords:: fatigue driving; detection method; multi-feature information fusion; review

Abstract:: Fatigue driving is a major cause of traffic accidents, which poses a great threat to public safety and property. In order to reduce the losses caused by fatigue driving, many researchers have devoted themselves to the study about fatigue driving, such as driver behavior monitoring, brainwave monitoring, eye tracking and facial expression analysis. Each of these methods has its own advantages and disadvantages. Behavioral monitoring reflects the fatigue state by analyzing the driver’s driving habits and facial expression, which is easy to operate but prone to be affected by the external environment. Brainwave monitoring is more accurate and can detect the fatigue state in real time, but the equipment is complicated and costly, which limits its large-scale application. The detection based on eye-tracking and facial expression analysis also has a certain potential for application, but errors may occur under different light conditions. Herein the review introduces the effects of fatigue on driving ability and compares the research results of various fatigue driving detection methods by searching, collating, analyzing and summarizing the relevant literatures at home and abroad. Various detection methods are analyzed and summarized, and it is pointed out that the fatigue driving detection method based on multi-feature information fusion will become a research hotspot.