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.

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In this paper, the correlation degree between driver’s reaction time and the physiological signal is analyzed. For this purpose, a large number of road experiments are performed using the biopac and the reaction time test systems to collect data. First, the electroencephalograph (EEG) signal is processed by using the fast Fourier and the inverse Fourier transforms. Then, the power spectrum densities (PSD) ofα,β,δ, and EEG wave are calculated by Welch procedure. The average power of the power spectrum ofα,β, andθis calculated by the biopac software and two ratio formulas,(α+θ)/βandα/β, are selected to be the impact factors. After that the heart rate and the standard deviation of RR interval are calculated from the electrocardiograph (ECG) signal. Lastly, the correlation degree between the eight impact factors and the reaction time are analyzed based on the grey correlation analysis. The results demonstrate thatα/βhas the greatest correlation to the reaction time except EEG-PSD. Furthermore, two mathematical models for the reaction time-driving time and theα/β-driving time are developed based on the Gaussian function. These mathematical models are then finally used to establish the functional relation ofα/β-the reaction time.

A clustering method that combined density-based spatial clustering of applications with noise with mathematical morphology clustering is proposed to adapt to the features of driver's fixation such as points' dispersion and fixation regions' irregularity and solve the problems of conventional density-based spatial clustering of applications with noise method's large influence by parameters and mathematical morphology clustering's needs of much manual intervention. Drivers' fixation data were collected by Smart Eye Pro 5.7 eye tracking system, and the data were processed and clustered using conventional clustering methods and density-based spatial clustering of applications with noise-mathematical morphology clustering method. The results show that the method proposed in this article takes into account the advantages of density-based spatial clustering of applications with noise and mathematical morphology clustering to cluster irregular regions and makes up for defects of conventional clustering methods. It is verified that density-based spatial clustering of applications with noise-mathematical morphology clustering method is better than the conventional hierarchical clustering method and density-based spatial clustering of applications with noise method in driver's fixation points clustering and can improve the quality of driver's fixation region division.

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