Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions

Beles, Horia; Vesselenyi, Tiberiu; Rus, Alexandru; Mitran, Tudor; Scurt, Florin Bogdan; Tolea, Bogdan Adrian

doi:10.3390/s24051541

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…This section reviews key advancements and methodologies in this domain, focusing on a holistic approach to driver state and behavior analysis and applications in related fields such as e-learning [16]. It must be noted that, despite our focus on vision systems, there are several other techniques for detecting drowsiness based on sensors, such as EEG [17][18][19], gyroscope data of head motion [18], heart rate [20], and others. Since we rely on the visual information captured in different spectra, any form of user collaboration is not expected, e.g., wearing additional body sensors or touching dedicated areas on the steering wheel.…”

Section: Related Workmentioning

confidence: 99%

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Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users

Smoliński,

Forczmański,

Nowosielski

2024

Electronics

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This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify driver drowsiness and distraction signs. Our novel contribution includes utilizing state-of-the-art convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks for effective feature extraction and classification across diverse distraction scenarios. Additionally, we explore various data fusion techniques, demonstrating their impact on improving detection accuracy. The significance of this work lies in its potential to enhance road safety by providing more reliable and efficient tools for the real-time monitoring of driver attentiveness, thereby reducing the risk of accidents caused by distraction and fatigue. The proposed methods are thoroughly evaluated using a multimodal benchmark dataset, with results showing their substantial capabilities leading to the development of safety-enhancing technologies for vehicular environments. The primary challenge addressed in this study is the detection of driver states not relying on the lighting conditions. Our solution employs multimodal data integration, encompassing RGB, thermal, and depth images, to ensure robust and accurate monitoring regardless of external lighting variations

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Section: Related Workmentioning

confidence: 99%

“…The success of these methods underscores deep learning's transformative potential in enhancing road safety. Innovations such as leveraging computer vision and eye-blink analyses and utilizing novel deep convolutional neural network models have shown great promise [19,27,28].…”

Section: Related Workmentioning

confidence: 99%

Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users

Smoliński,

Forczmański,

Nowosielski

2024

Electronics

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“…In the domain of vehicular safety, the current research is focused on reducing accidents caused by driver somnolence through three primary detection methodologies: physiological metrics (including electroencephalography (EEG) [2,3], electrooculography [4], and multi-modal algorithms [5]); vision-based methods (tracking eyelid movement, yawning, and head orientation) [6][7][8][9][10]; and vehicular dynamics analysis (such as lateral position and speed variation) [11,12]. Although vehicular dynamics offer an indirect measure of alertness, physiological and optical methods provide a more direct assessment.…”

Section: Introductionmentioning

confidence: 99%

Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny

Chang,

Zeng,

Lin

2024

Applied Sciences

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This study refines the YOLOv7-tiny model through structured pruning and architectural fine-tuning, specifically for real-time eye state detection. By focusing on enhancing the model’s efficiency, particularly in environments with limited computational resources, this research contributes significantly to advancing driver monitoring systems, where timely and accurate detection of eye states such as openness or closure can prevent accidents caused by drowsiness or inattention. Structured pruning was utilized to simplify the YOLOv7-tiny model, reducing complexity and storage requirements. Subsequent fine-tuning involved adjustments to the model’s width and depth to further enhance processing speed and efficiency. The experimental outcomes reveal a pronounced reduction in storage size, of approximately 97%, accompanied by a sixfold increase in frames per second (FPS). Despite these substantial modifications, the model sustains high levels of precision, recall, and mean average precision (mAP). These improvements indicate a significant enhancement in both the speed and efficiency of the model, rendering it highly suitable for real-time applications where computational resources are limited.

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Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny

Chang,

Zeng,

Lin

2024

Preprint

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This study demonstrates the efficacy of structured pruning and architectural fine-tuning on the YOLOv7-tiny model for eye state detection, emphasizing optimization for real-time applications. Structured pruning significantly reduced the model's complexity and storage size while maintaining high detection accuracy, as evidenced by stable precision, recall, and mAP@.5 metrics across iterations. Further fine-tuning adjusted the model's width and depth, optimizing efficiency and processing speed without compromising performance. These optimizations yielded YOLOv7-tiny variants that are both computationally efficient and accurate, suitable for resource-constrained environments. The findings highlight the critical role of model optimization in deploying effective neural networks for specific detection tasks in real-time scenarios.

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Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions

Cited by 4 publications

References 36 publications

Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users

Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users

Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny

Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny

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