Effects of Prolonged Tasks and Rest Patterns on Driver's Visual Behaviors, Driving Performance, and Sleepiness Awareness in Tunnel Environments: A Simulator Study

Wang, Yong Gang; Cheng-xi, MA; Li, Yanhui

doi:10.1007/s40996-018-0093-4

Cited by 12 publications

(9 citation statements)

References 26 publications

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“…This is mainly due to the fact that they are more susceptible to become fatigued while driving carefully. Similar findings have also been reported in our previous research [5,6].…”

Section: Conclusion and Discussionsupporting

confidence: 93%

“…The results indicate that the variation of driver fatigue levels is positively correlated with the change of pupil diameter, fixations durations, and blinking behaviors (e.g., blink frequency, blink duration, closure duration), and negatively related to the change of the number of fixations, deviations of the visual search angle, and saccade behaviors (i.e., saccade number, saccade speed and saccade amplitude), which are consistent with the results reported in our previous research [5,6].…”

Section: Conclusion and Discussionsupporting

confidence: 91%

“…In many previous studies, a great number of researchers have used visual signals such as degree of eye opening, blink duration, gaze, yawning, etc., to assess the level of driving fatigue [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, some others have explored physiological measures such as brain waves, heart rate, breathing, body temperature, and blood pressure to examine driver fatigue [7][8][9]. Furthermore, the variation of driving behaviors (e.g., steering wheel movements, deviation of lateral position, acceleration/deceleration) is also frequently used in identifying driver fatigue [6,10].…”

Section: Introductionmentioning

confidence: 99%

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Automation Detection of Driver Fatigue Using Visual Behavior Variables

Wang

2018

Archives of Civil Engineering

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To examine the correlation of driver visual behaviors and subjective levels of fatigue, a total of 36 commercial drivers were invited to participate in 2-h, 3-h, and 4-h naturalistic driving tests during which their eye fixation, saccade, blinking variables, and self-awareness of their fatigue levels were recorded. Then, one-way ANOVA was applied to analyze the variations of each variable among different age groups over varying time periods. The statistical analysis revealed that driving duration had a significant effect on the variation of visual behaviors and feelings of fatigue. After 2h of driving, only the average closure duration value and subjective level of fatigue had an increase of one-fifth or more. After 4h of driving, however, all these variables had a significant change except for the number of saccades and pupil diameter measurements. Particularly, driver saccadic eye movement was more sensitive to driving fatigue, and the elderly were more likely to be affected by the duration of the drive. Finally, a predictor of driver fatigue was determined to detect the real-time level of fatigue and alert at the critical moment.

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“…This is mainly due to the fact that they are more susceptible to become fatigued while driving carefully. Similar findings have also been reported in our previous research [5,6].…”

Section: Conclusion and Discussionsupporting

confidence: 93%

Section: Conclusion and Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automation Detection of Driver Fatigue Using Visual Behavior Variables

Wang

2018

Archives of Civil Engineering

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“…Taxi companies are strongly suggested to set up special funds to help those engaged in injury crashes. Furthermore, both the government and taxi companies should develop stricter rules and regulations to limit registered drivers' maximum daily and weekly hours of work, which may help to prevent fatigued driving [21,22].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Predicting personal injury crash risk through working conditions, job strain, and risky driving behaviors among taxi drivers

Zhou

Wang

2018

Eur. Transp. Res. Rev.

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Introduction: Taxis play an important role among public transport modes in China, but there has been very little in-depth research regarding taxi drivers' crash risk. Thus, this study aimed to develop a quantitative method to predict taxi drivers' crash risk and identify contributory factors. Methods: Nine hundred fourty-eight professional taxi drivers in Xi'an, China completed an anonymous, structured face-to-face questionnaire reporting their demographic information, work-related stress, daily risky driving behaviors, and crash experience within the 3 years prior to the survey. A Negative-Binomial regression model was used to predict the risk of personal injury collisions for taxi drivers. Results: Drivers' 7 risky driving behaviors (e.g., disregarding red lights, speeding, aggressive driving, driving while sleepy or fatigued, etc.) were significantly and positively related to the risk of personal injury collisions, while driver's parking at will to pick up/drop off passengers was not a significant predictor of such risk for taxi drivers. Furthermore, driver's sociodemographic characteristics and level of occupational workload were not found to be significantly correlated with the personal injury crash risk. Conclusions: Risk traits appear to peak among male taxi drivers who drive more hours per day, pay high management fees, and frequently engage in risky behaviors while driving. These findings provide implications to design potentially useful policy initiatives as well as targeted safety promotion programs to prevent road crashes involving professional taxi drivers.

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Research on safe driving behavior of transportation vehicles based on vehicle network data mining

Sun

Han

et al. 2019

Trans Emerging Tel Tech

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In recent years, with the rapid development of China's economy and the growth of people's travel demand, road transport industry has developed rapidly. However, at the same time, the traffic safety problems caused by the accelerated growth of transport vehicles are becoming more and more serious. Driver's driving behavior is closely related to traffic safety. With the help of data mining technology, we can make full use of the system data collected by road transportation industry to identify the bad driving behavior of transport vehicles and get seven secondary indicators of traffic safety evaluation indicators: overspeed, accelerated, decelerated, fatigue driving, idle preheating, overspeed idling, and flameout taxiing. According to the indicators, the occurrence times and duration are further subdivided into 14 three‐level indicators. Then, by using the analytic hierarchy process, through the construction of comparison judgment matrix and consistency checking, the corresponding weight of each evaluation index is determined with the help of the entropy method, and the formation safety of transport vehicles is evaluated. The results show that drivers with more fatigue driving, acceleration, and deceleration have higher driving risk. Transportation enterprises and relevant departments need to focus on their monitoring. The combination of data mining, data analysis algorithm, and traffic safety field in machine learning provides a demonstration for the study of safe driving behavior of transport vehicles in China and has a guiding significance for the supervision and training of drivers' driving behavior and reducing road safety risks.

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Effects of Prolonged Tasks and Rest Patterns on Driver's Visual Behaviors, Driving Performance, and Sleepiness Awareness in Tunnel Environments: A Simulator Study

Cited by 12 publications

References 26 publications

Automation Detection of Driver Fatigue Using Visual Behavior Variables

Automation Detection of Driver Fatigue Using Visual Behavior Variables

Predicting personal injury crash risk through working conditions, job strain, and risky driving behaviors among taxi drivers

Research on safe driving behavior of transportation vehicles based on vehicle network data mining

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