PERCLOS Threshold for Drowsiness Detection during Real Driving

Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.

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“…Percentage of eyelid closure (PERCLOS) [31] The percentage of time (per minute) in which the eye is 80% closed or more.…”

Section: Features Descriptionmentioning

confidence: 99%

“…The device's processing model, which was performed over a Raspberry Pi 3 Model B, combines features obtained from the eyes and mouths of the subjects under consideration. The features include PERCLOS [31], eye closing duration, and average mouth opening time. The 300-W dataset [53] was used in the training process.…”

mentioning

confidence: 99%

A Review of Recent Developments in Driver Drowsiness Detection Systems

Albadawi

Takruri

Awad

2022

Sensors

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“…The EAR value of an open eye is higher than that of a closed eye. The system uses the percentage of eye closure (PERCLOS) [28] metric values to measure the alertness of drivers. PERCLOS is a fatigue detection metric that calculates eye closure rate over the pupil in real-time.…”

Section: The Methods and Proposed Systemmentioning

confidence: 99%

Computer vision-based approach to detect fatigue driving and face mask for edge computing device

Rahman

Hriday

Khan

2022

Heliyon

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“…In the driver's inattention research field, potential direct measures such as physiological signals sensors (Akin et al, 2008;Wesley et al, 2010) and visual behaviors observation (Bergasa et al, 2008;Doshi and Trivedi, 2012;Lin et al, 2012) can be used to detect the degree of driver attention or distraction. In (Imamura et al, 2008) and (Ueno et al, 1994), driver drowsiness was indirectly detected through vehicle speed, steering wheel motion, lateral position, and acceleration pedal states.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Visual Assistance System for Enhancing the Driver Awareness of Pedestrians

Frémont

Phan

Thouvenin

2019

International Journal of Human–Computer Interaction

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In the past decade, Pedestrian Collision Warning Systems have been proposed to detect pedestrians and warn drivers of imminent collision. However, such systems are often limited by eye-off-road and cognitive overload problems. Head Up displays with augmented reality are being considered as a key technology for changing drivers' user experiences. In this paper, we propose a new visual assistance system that can enhance drivers' perception by dynamically directing attention to pedestrians to avoid collisions using Augmented Reality cues. The proposed system takes into account driver behaviors through vehicle driving signals analysis, in order to warn it at the right moments. To that end, we statistically model correct and incorrect driver behaviors in situations with pedestrians. Based on this model, a warning visual metaphor is displayed if unawareness is detected and a driving simulator was used to evaluate the concept. The experimental results suggest that our proposed adaptive visual aids can enhance driver awareness of pedestrians in critical situations.

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PERCLOS Threshold for Drowsiness Detection during Real Driving

Cited by 23 publications

References 0 publications

A Review of Recent Developments in Driver Drowsiness Detection Systems

A Review of Recent Developments in Driver Drowsiness Detection Systems

Computer vision-based approach to detect fatigue driving and face mask for edge computing device

Adaptive Visual Assistance System for Enhancing the Driver Awareness of Pedestrians

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