Driving Fatigue Prediction Model considering Schedule and Circadian Rhythm
| Autor: | Qi Zhang Zhang, Chaozhong Wu Wu, Hui Zhang Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Economics and Econometrics
Schedule Article Subject Computer science Strategy and Management Poison control Standard deviation 03 medical and health sciences 0302 clinical medicine 0502 economics and business Linear regression Range (statistics) Performance measurement Circadian rhythm Simulation HE1-9990 050210 logistics & transportation TA1001-1280 Mechanical Engineering 05 social sciences Regression analysis Computer Science Applications Transportation engineering Automotive Engineering Transportation and communications 030217 neurology & neurosurgery |
| Zdroj: | Journal of Advanced Transportation, Vol 2020 (2020) |
| ISSN: | 2042-3195 0197-6729 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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