The effect of fatigue driving on car following behavior

Zhang, Hui; Wu, Chaozhong; Yan, Xinping; Qiu, Tony Z.

doi:10.1016/j.trf.2016.06.017

Cited by 38 publications

(14 citation statements)

References 13 publications

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“…Three behavioral indices were calculated to indicate performance in a Go/NoGo tasks: the reaction time (RT) for correct trials, the miss rate for Go stimuli, and the false alarm rate for the NoGo stimuli. Subjective ratings of mental fatigue before and after fatigue manipulation task and the percentage of eye closure (PERCLOS) [ 38 ] and standard deviation of the lane position (SDLP) [ 39 ] in the first and last 30 mins of the simulated driving task were also calculated to reflect the level of mental fatigue. The formulas of reaction time, miss rate, PERCLOS and SDLP are shown in the following: Here, RT was the mean value of the reaction time on Go stimuli, RT k is the reaction time on Go stimuli at number k , n 1 is the amount of Go stimuli that corresponding to correctly.…”

Section: Methodsmentioning

confidence: 99%

The impairing effects of mental fatigue on response inhibition: An ERP study

et al. 2018

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Mental fatigue is one of the main reasons for the decline of response inhibition. This study aimed to explore the impairing influence of mental fatigue on a driver’s response inhibition. The effects of mental fatigue on response inhibition were assessed by comparing brain activity and behavioral indices when performing a Go/NoGo task before and after a 90-min fatigue manipulation task. Participants in the driving group performed a simulated driving task, while individuals in the control group spent the same time watching movies. We found that participants in the driving group reported higher levels of mental fatigue and had a higher percentage of eye closure and larger lateral deviations from their lane positions, which indicated there was effective manipulation of mental fatigue through a prolonged simulated driving task. After manipulation of mental fatigue, we observed increased reaction time and miss rates, delayed NoGo-N2 latency and Go-P3 latency, and decreased NoGo-P3 amplitude, which indicated that mental fatigue may slow down the speed of the inhibition process, delay the evaluation of visual stimuli and reduce the availability of attentional resources. These findings revealed the underlying neurological mechanisms of how mental fatigue impaired response inhibition.

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Section: Methodsmentioning

confidence: 99%

The impairing effects of mental fatigue on response inhibition: An ERP study

et al. 2018

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“…e trends of SDLP of the two participants with 5.5 and 3.8 sleep hours are similar, especially during the period before having a break. 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.…”

Section: Verification Of the Fatigue Value Prediction Modelmentioning

confidence: 99%

“…With the aim to detect or predict the fatigue status of drivers, more measurements including contextual, contact, or contactless physiological features; driver behavior; vehicle maneuver; and environment [17,18], and more complex mathematical algorithms [17,19,20] were proposed. Whether in simulation experiments or field study, devices such as eye trackers and electroencephalograph recorders were equipped to collect data that will be used in algorithms [21][22][23]. All these studies are processing new algorithms, based on probability or statistics models and specific indicators, driving fatigue detection and prediction models were established through data mining.…”

Section: Introductionmentioning

confidence: 99%

Driving Fatigue Prediction Model considering Schedule and Circadian Rhythm

Zhang

2020

Journal of Advanced Transportation

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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.

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“…Since an electrocardiogram (ECG) is easier to measure than EEG and measures autonomic nervous system activity, ECG-based methods have been proposed [11], along with hybrid methods of both EEG and ECG measures [12]. In addition, previous studies showed that drowsiness level affects driving performance [13][14][15]. The movement of the steering wheel is mainly utilized to evaluate driving performance and detect driver drowsiness [13].…”

Section: Introductionmentioning

confidence: 99%

“…As an example, the standard deviation of lateral position (SDLP), which is widely utilized as the evaluation index of steering control, increases [14]. Performance related to preceding-car following such as Time Headway (THW), which is defined as the time between successive vehicles that pass a certain point in the path of traffic flow, decreases [15]. To determine the drowsiness level of a driver based on these known indices, machine learning algorithms have been widely used.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing

2020

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Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First, we measured the drowsiness level, driving performance, physiological signals (from electroencephalogram and electrocardiogram results), and behavioral indices of a driver using a driving simulator and driver monitoring system. Next, driver alert and drowsy states were identified by machine learning algorithms, and a dataset was constructed from the extracted indices over a period of 10 s. Finally, ensemble algorithms were used for classification. The results showed that the ensemble algorithm can obtain 82.4% classification accuracy using hybrid methods to identify the alert and slightly drowsy states, and 95.4% accuracy classifying the alert and moderately drowsy states. Additionally, the results show that the random forest algorithm can obtain 78.7% accuracy when classifying the alert vs. slightly drowsy states if physiological indicators are excluded and can obtain 89.8% accuracy when classifying the alert vs. moderately drowsy states. These results represent the feasibility of highly accurate early detection of driver drowsiness and the feasibility of implementing a driver drowsiness detection system based on hybrid sensing using non-contact sensors.

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The effect of fatigue driving on car following behavior

Cited by 38 publications

References 13 publications

The impairing effects of mental fatigue on response inhibition: An ERP study

The impairing effects of mental fatigue on response inhibition: An ERP study

Driving Fatigue Prediction Model considering Schedule and Circadian Rhythm

An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing

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