To determine the status of fatigue and the speed of fatigue accumulation in drivers of different ages during driving, we compared the difference in fatigue generation and change to obtain the optimal driving time for drivers of different ages, and a natural driving experiment was designed in our study. The electroencephalogram (EEG) data of drivers was collected through Physio, a physiological multichannel instrument. The drivers were inquired by the subjective test method simultaneously. MATLAB was applied to denoise the collected EEG data and the average densities of power spectrum of the α wave, β wave, and θ wave in each period were calculated through integration. Subsequently, the EEG indexes, R(α/β), R(θ/β), and R(α+θ)/β, were obtained. The one-way analysis of variance (ANOVA) was performed by comparing them with the driving time using SPSS. R(α+θ)/β was chosen as the characterization index of driving fatigue through sensitivity judgment. The R(α+θ)/β values in drivers of different ages were averaged and were fitted linearly with the driving time such that the impact of the driver's age on the speed of driving fatigue accumulation can be analyzed. The t tests of paired samples were performed on R(α+θ)/β in each period during driving combined with the subjective inquiry results; the optimal driving time in drivers of different ages was obtained. The result shows that the speed of fatigue accumulation of young and middle-aged drivers within 0-1.5 h is relatively slow, while that of the elderly drivers is fast. However, that of the young drivers is the fastest within 1.5-3 h, while that of the middle-aged drivers is the slowest. The optimal driving time of the elderly, middle-aged and young drivers is 60-75 min, 120-135 min, and 105-120 min. Driving experience, physical strength, the energy in drivers of different ages, and the disturbance of external environment are the important factors that affect the speed of fatigue accumulation. The experiment results verify the effectiveness of using R(α+θ)/β as the driving fatigue characterization index, which will help to afford scientific evidence to set a safe driving time for drivers of different ages.
Key words
traffic engineering /
driving fatigue accumulation /
driving experiment /
driver's age
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