Chromatic analysis of signals from a driver fatigue monitoring unit

Transportation Research Record: Journal of the Transportation R

Yan

et al. 2014

Circadian rhythms, inherent in all humans, consist of 24-h biological patterns that affect a person's fatigue level. The effect of circadian rhythms on driving performance was explored in an on-road driving study. Fifteen middle-aged professional daytime drivers were recruited to participate in the experiment. Participants were classified into three groups: (a) a morning group that started driving at 09:00, (b) a noon group that started driving at 12:00, and (c) an evening group that started driving at 21:00. Each group completed a 6-h driving task. The self-reported Karolinska sleepiness scale score was recorded every 5 min, and data on driving performance parameters, such as steering and lane positioning, were also acquired. The results indicated that both circadian rhythms and driving duration had significant effects on self-reported fatigue levels and that the fatigue level increased faster in the evening group than the morning and noon groups. The results of the circadian rhythm analysis showed that a driver was most likely to feel tired between 14:00 and 16:00 and between 02:00 and 04:00, when the ability to stay within designated lane lines (lane maintenance) was significantly impaired for drivers in all three groups. The evening group drivers were the most at risk. The steering performance did not show a significant relationship with the self-reported fatigue level. The self-reported fatigue level is the result of the interactive effect of circadian rhythms and driving duration. The standard deviation of lane position was more correlated with circadian rhythms than with the steering reversal rate.

Section: Circadian Rhythms Driving Duration and Fatigue Levelsupporting

confidence: 93%

Effect of Circadian Rhythms and Driving Duration on Fatigue Level and Driving Performance of Professional Drivers

Transportation Research Record: Journal of the Transportation R

Yan

et al. 2014

“…The linear model was established between fatigue levels and driver performance from lateral acceleration data obtained from a gyroscope sensor, as discussed by Koh et al (23). Also, one combination linear model was established between the drowsiness level and the driving performance measurement (12).…”

Section: Modelmentioning

confidence: 99%

Sensitivity of Lane Position and Steering Angle Measurements to Driver Fatigue

Transportation Research Record: Journal of the Transportation R

Huang

et al. 2016

The parameter value chosen to measure driving performance affects the accuracy of the estimated fatigue level. Methods to analyze the sensitivity of these parameter values were proposed. Standard deviation of lane position (SDLP) and steering reversal rate (SRR) were considered to assess fatigue, and the sensitivity of these parameters was analyzed from the time domain and value domain. Thirty-six male drivers participated in a field test. Lane position, steering wheel angle data, and self-reported fatigue level (scored on the Karolinska sleepiness scale) were recorded. SDLP results indicate that the maximum average coefficient with fatigue level reached .11, with a unified statistical interval of 202 s when the consecutive analysis method was used; the maximum average coefficient was .12 with a unified interval of 120 s when the maximum analysis method was used. SRR results indicate that a steering angle difference of 6° was the most sensitive threshold for driver fatigue level and has an average correlation coefficient of .42, which demonstrated that SRR was more reliable than SDLP for monitoring fatigue level. With the use of the optimal parameter value, the variation results of SDLP and SRR at each fatigue level were examined, and results indicate that driving ability was impaired as fatigue level increased. The methods and results can be applied to analyses of fatigued or drowsy driving.

“…By analyzing the recorded video of the 34 selected participants, the driver's consecutive driving time, rest time, and the circadian rhythm range during the schedules could be obtained; the real-time fatigue value could be linearly summed to give overall tiredness scores [49], and the basic relationship between the four variables is shown in…”

Section: Experimental Design and Data Processingmentioning

confidence: 99%

“…Rhythm. According to the previous research work, it could be seen that the fatigue value caused by circadian rhythm shows a regular change with the time of the day [36,49,51,52]; in this study, it is assumed to be the same among all participants as an underlying fixed variable. Firstly, the missing circadian rhythm data are interpolated by cubic spline interpolation, and then the effects of circadian rhythm shown in Figure 7 were best fitted with a sum of sin functions shown as…”

Section: Modeling Of Fatigue Value Contributed By Circadianmentioning

confidence: 99%

“…In this study, 0.3 was considered as the SDLP threshold [23,53], and the three participants exceeded the threshold during the later driving stage in their schedules. When 100 was seen as the participants' fatigue value threshold [49,51,52], only the SDLP of the participants with 7.4 hours of sleep is less than the threshold. In addition, the shorter the participants' sleep, the more the SDLP exceeds the threshold.…”

Section: Verification Of the Fatigue Value Prediction Modelmentioning

confidence: 99%

See 1 more Smart Citation

Driving Fatigue Prediction Model considering Schedule and Circadian Rhythm

Journal of Advanced Transportation

2020

Driver fatigue level was considered an accumulated result contributed by circadian rhythms, hours of sleep before driving, driving duration, and break time during driving. This article presents an investigation into the regression model between driver fatigue level and the above four time-related variables. With the cooperation of one commercial transportation company, a Naturalistic Driving Study (NDS) was conducted, and NDS data from thirty-four middle-aged drivers were selected for analysis. With regard to the circadian rhythms, commercial drivers operated the vehicle and started driving at around 09:00, 14:00, and 21:00, respectively. Participants’ time of sleep before driving is also surveyed, and a range from 4 to 7 hours was selected. The commercial driving route was the same for all participants. After getting the fatigue level of all participants using the Karolinska Sleepiness Scale (KSS), the discrete KSS data were converted into consecutive value, and curve fitting methods were adopted for modeling. In addition, a linear regression model was proposed to represent the relationship between accumulated fatigue level and the four time-related variables. Finally, the prediction model was verified by the driving performance measurement: standard deviation of lateral position. The results demonstrated that fatigue prediction results are significantly relevant to driving performance. In conclusion, the fatigue prediction model proposed in this study could be implemented to predict the risk driving period and the maximum consecutive driving time once the driving schedule is determined, and the fatigue driving behavior could be avoided or alleviated by optimizing the driving and break schedule.