Driver Drowsiness Detection Techniques: A Survey

Hussein, Marwa; Salman, Tariq M.; Miry, Abbas H.; Subhi, Mohammed Ahmed

doi:10.1109/bicits51482.2021.9509912

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…Since vehicle-based systems issue alerts later in the initial drowsiness phase, potentially limiting accident prevention opportunities, relying solely on this technique is not advisable. Instead, combining it with other methods to detect a driver’s drowsiness proves to be more effective [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…It can be calculated using built-in algorithms in eye-tracking systems (ETSs) like SmartEye [ 27 ] or via image processing from recorded facial videos [ 18 , 19 ]. However, limitations in image processing include challenges with video/image quality, eyewear interference, varying lighting, and head movement, impacting performance [ 24 , 26 , 27 ]. Addressing these constraints is crucial, as relying solely on this technique may lack reliability.…”

Section: Introductionmentioning

confidence: 99%

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Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Minhas,

Peker,

Hakkoz

et al. 2024

Sensors

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Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta–alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta–alpha-ratio (87.2%) and delta–theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta–alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Minhas,

Peker,

Hakkoz

et al. 2024

Sensors

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“…Driver drowsiness is defined as a state of sleepiness when the driver needs to rest, and it can cause symptoms that have a great impact on the performance of tasks, such as intermittent lack of awareness, slowed response time, or microsleeps. While driving, these symptoms are highly dangerous, as they greatly increase the odds of drivers missing exits or road signs, drifting into other lanes, or even crashing their vehicle, causing an accident [ 1 , 2 , 3 ]. According to the American Automobile Association (AAA), the Foundation for Traffic Safety in the United States reported that driver drowsiness was responsible for 23.5% of all automobile crashes recorded in 2015; 16.5% were fatal crashes, and 7% were non-fatal crashes [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers have proposed systems to be installed in cars to detect driver drowsiness, motivated by the urgent need to limit the number of traffic crashes related to driver drowsiness. Different approaches have been used in research to detect drivers’ drowsiness, including physical features, physiological features, vehicle-based implementations, and hybrid approaches [ 3 ]. The physical features approach is the most frequently used to detect drivers’ drowsiness, using features such as eye tracking, yawning, head position, and detecting facial landmarks to detect drowsiness [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Different approaches have been used in research to detect drivers’ drowsiness, including physical features, physiological features, vehicle-based implementations, and hybrid approaches [ 3 ]. The physical features approach is the most frequently used to detect drivers’ drowsiness, using features such as eye tracking, yawning, head position, and detecting facial landmarks to detect drowsiness [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning

Alajlan

Ibrahim

2023

Sensors

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Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT devices, which pose a challenge to fulfilling DL models that demand large storage and computation. Thus, there are challenges to meeting the requirements of real-time driver drowsiness detection applications that need short latency and lightweight computation. To this end, we applied Tiny Machine Learning (TinyML) to a driver drowsiness detection case study. In this paper, we first present an overview of TinyML. After conducting some preliminary experiments, we proposed five lightweight DL models that can be deployed on a microcontroller. We applied three DL models: SqueezeNet, AlexNet, and CNN. In addition, we adopted two pretrained models (MobileNet-V2 and MobileNet-V3) to find the best model in terms of size and accuracy results. After that, we applied the optimization methods to DL models using quantization. Three quantization methods were applied: quantization-aware training (QAT), full-integer quantization (FIQ), and dynamic range quantization (DRQ). The obtained results in terms of the model size show that the CNN model achieved the smallest size of 0.05 MB using the DRQ method, followed by SqueezeNet, AlexNet MobileNet-V3, and MobileNet-V2, with 0.141 MB, 0.58 MB, 1.16 MB, and 1.55 MB, respectively. The result after applying the optimization method was 0.9964 accuracy using DRQ in the MobileNet-V2 model, which outperformed the other models, followed by the SqueezeNet and AlexNet models, with 0.9951 and 0.9924 accuracies, respectively, using DRQ.

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