Video-based observer rated sleepiness versus self-reported subjective sleepiness in real road driving

Ahlström, Christer; Fors, Carina; Anund, Anna; Hallvig, David

doi:10.1007/s12544-015-0188-y

Cited by 25 publications

(17 citation statements)

References 32 publications

(52 reference statements)

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“…For those reasons, precise definition of a state to correspond the rate score is sometimes compromised. This is confirmed by a study, where results have demonstrated poor correlation of ORS to SRS (self-reported sleepiness) [26]. It is still possible to track a tendency of an increasing sleepiness rate per suggested scale after the 40th minute of driving, which corresponds to conclusions mentioned in [25].…”

Section: Subjective Evaluation During Experiments On Driver Simulatorsupporting

confidence: 78%

Subjective Methods for Assessment of Driver Drowsiness

Mashko

2017

APP

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Abstract. The paper deals with the issue of fatigue and sleepiness behind the wheel, which for a long time has been of vital importance for the research in the area of driver-car interaction safety. Numerous experiments on car simulators with diverse measurements to observe human behavior have been performed at the laboratories of the faculty of the authors. The paper provides analysis and an overview and assessment of the subjective (self-rating and observer rating) methods for observation of driver behavior and the detection of critical behavior in sleep deprived drivers using the developed subjective rating scales.

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“…For driver self-ratings, questionnaires such as the Karolinska Sleepiness Scale (KSS) [9], the Epworth Sleepiness Scale (ESS) [10], the Stanford Sleepiness Scale (SSS) [11], or the Visual Analog Scale (VAS) [12] are provided. For observer ratings, experts or trained raters observe the driver either in real-time [13] or by watching videos recorded during an experiment [14]. They evaluate the driver's current drowsiness state with scales that focus on sleep-induced indicators in the facial region [7,8,14,15].…”

Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

“…For observer ratings, experts or trained raters observe the driver either in real-time [13] or by watching videos recorded during an experiment [14]. They evaluate the driver's current drowsiness state with scales that focus on sleep-induced indicators in the facial region [7,8,14,15]. Since this type of measurement depends either on the driver himself/herself or an external observer, the usage in a real-world driving scenario is not possible.…”

Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

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Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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“…Another widely used and less subjective method is the observer's assessment of tiredness status. Different rating scales and different time periods have been used for the assessment (see Naujoks et al (2018), Wierwille and Ellsworth (1994), Ahlstrom et al (2015)). The most frequently used scales are the Observer Rating of Drowsiness (ORD) developed by Wierwille & Ellsworth (1994) or the Observer Rated Sleepiness (ORS - Anund et al 2013).…”

Section: General Considerationsmentioning

confidence: 99%

Driving and tiredness: Results of the behaviour observation of a simulator study with special focus on automated driving

Kaufmann¹,

Frühwirth²,

Messerschmidt³

et al. 2020

ToTS

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“…Therefore, the current study also filtered out records with noticeable driver impairment from drugs, alcohol, drowsiness, etc., in its queries of the online SHRP 2 version 2.1.1 crash and baseline databases. Note that driver impairments were tabulated in the databases based on a 20-s window, rather than the 6-s window for secondary tasks and driver behavior errors, because it is difficult to identify impairments such as drowsy driving in short time windows (Ahlstrom et al, 2015) [9].…”

Section: Impairmentsmentioning

confidence: 99%

Talking on a Wireless Cellular Device While Driving: Improving the Validity of Crash Odds Ratio Estimates in the SHRP 2 Naturalistic Driving Study

Young¹

2017

Safety

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Dingus and colleagues (Proc. Nat. Acad. Sci. USA. 2016, 113, 2636-2641) reported a crash odds ratio (OR) estimate of 2.2 with a 95% confidence interval (CI) from 1.6 to 3.1 for hand-held cell phone conversation (hereafter, "Talk") in the SHRP 2 naturalistic driving database. This estimate is substantially higher than the effect sizes near one in prior real-world and naturalistic driving studies of conversation on wireless cellular devices (whether hand-held, hands-free portable, or hands-free integrated). Two upward biases were discovered in the Dingus study. First, it selected many Talk-exposed drivers who simultaneously performed additional secondary tasks besides Talk but selected Talk-unexposed drivers with no secondary tasks. This "selection bias" was removed by: (1) filtering out records with additional tasks from the Talk-exposed group; or (2) adding records with other tasks to the Talk-unexposed group. Second, it included records with driver behavior errors, a confounding bias that was also removed by filtering out such records. After removing both biases, the Talk OR point estimates declined to below 1, now consistent with prior studies. Pooling the adjusted SHRP 2 Talk OR estimates with prior study effect size estimates to improve precision, the population effect size for wireless cellular conversation while driving is estimated as 0.72 (CI 0.60-0.88).

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Video-based observer rated sleepiness versus self-reported subjective sleepiness in real road driving

Cited by 25 publications

References 32 publications

Subjective Methods for Assessment of Driver Drowsiness

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Driving and tiredness: Results of the behaviour observation of a simulator study with special focus on automated driving

Talking on a Wireless Cellular Device While Driving: Improving the Validity of Crash Odds Ratio Estimates in the SHRP 2 Naturalistic Driving Study

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