Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue

Abstract

Background: Driving fatigue affects the reaction ability of a driver. The aim of this research is to analyze the relationship between driving fatigue, physiological signals and driver’s reaction time. Methods: Twenty subjects were tested during driving. Data pertaining to reaction time and physiological signals including electroencephalograph (EEG) were collected from twenty simulation experiments. Grey correlation analysis was used to select the input variable of the classification model. A support vector machine was used to divide the mental state into three levels. The penalty factor for the model was optimized using a genetic algorithm. Results: The results show that α/β has the greatest correlation to reaction time. The classification results show an accuracy of 86%, a sensitivity of 87.5% and a specificity of 85.53%. The average increase of reaction time is 16.72% from alert state to fatigued state. Females have a faster decrease in reaction ability than males as driving fatigue accumulates. Elderly drivers have longer reaction times than the young. Conclusions: A grey correlation analysis can be used to improve the classification accuracy of the support vector machine (SVM) model. This paper provides basic research that online detection of fatigue can be performed using only a simple device, which is more comfortable for users.

A macroscopic traffic model for traffic flow harmonization

“…Examples of sluggish drivers are intoxicated or distracted drivers, old and new drivers, and drivers affected by fatigue [22,23]. The reaction time in a fatigued state has been shown to be 17% longer than in an alert state, and the increase in reaction time is greater for females than males [24]. Further, old drivers have longer reaction times than young drivers.…”

A Study of Integrated Driver fatigue Judging Algorithm Based on Principal Component Analysis

“…Nowadays, driving fatigue evaluation algorithm and system needs more in-depth research to enhance the robustness and generalization capability of the system to reduce the incidence of accidents caused by fatigue [10].…”

Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art

“…SVM is the most common method used for MF classification in the frequency domain. Besides its use with linear kernels, it was also extensively applied with non-linear kernels, for more robust classification performance [63], [64], [66], [67], [71], [76]. Some more specialized versions of the traditional SVM algorithm were also used.…”

“…On the surveyed literature, few authors explored the use of intermediate MF states. We can identify the studies using three [59], [76], [89] or four [67], [77], [91] different MF states. Some methods capable of performing regression can present an almost continuous MF development output [78], [79].…”