DRIVER DROWSINESS DETECTION

Registration ID: IJNRD_218954

Published ID: IJNRD2404620

DOI: Click Here to Get

Udhayakumar M , Sudhakaran R , Nishanth M , Priya V , Tharun T

Drowsiness Detection, DriverFatigue, Eye Tracking, Facial Recognition, EEG Signals, Machine Learning, Artificial Intelligence, In Vehicle Monitoring, Wearable Sensor, Reaction Time, Departure Detection.

Driver drowsiness is a significant factor contributing to road accidents worldwide. To mitigate the risks associated with drowsy driving, various techniques and technologies have been developed for early detection and alerting of drowsy drivers. This paper presents a comprehensive review of existing methodologies and advancements in driver drowsiness detection systems. The review encompasses traditional approaches such as monitoring physiological signals including eye movements, heart rate variability, and EEG signals, as well as modern techniques leveraging computer vision, machine learning, and sensor fusion methods. We examine the strengths and limitations of each approach, highlighting their effectiveness in real-world scenarios. Furthermore, we discuss the challenges associated with drowsiness detection systems, including variability in individual drowsiness patterns, environmental factors, and system robustness. Insights into emerging trends such as deep learning, wearable sensors, and in-vehicle monitoring systems are also provided. By synthesizing the current state-of-the-art, this review aims to provide researchers and practitioners with a comprehensive understanding of driver drowsiness detection techniques, fostering the development of more reliable and effective systems to enhance road safety. Physiological Signals Monitoring: This technique involves monitoring various physiological signals such as eye movements (blink rate, eyelid closure duration), heart rate variability (HRV), and electroencephalogram (EEG) signals to assess the driver's drowsiness level. Changes in these signals can indicate fatigue and drowsiness.Computer Vision: Computer vision-based systems analyze facial features and driver behavior captured by in-vehicle cameras to detect signs of drowsiness, such as drooping eyelids, yawning, and head nodding. Advanced algorithms can accurately identify these cues even under varying lighting conditions and facial orientations. Machine Learning: Machine learning algorithms, particularly classifiers such as support vector machines (SVM), random forests, and deep neural networks, are used to process data from physiological sensors or computer vision systems and classify the driver's drowsiness level based on learned patterns and features. Sensor Fusion: Sensor fusion techniques integrate data from multiple sources, such as physiological sensors, cameras, steering wheel sensors, and vehicle movement sensors, to enhance the accuracy and reliability of drowsiness detection systems. By combining information from different modalities, these systems can provide more robust assessments of driver alertness. Real-Time Alerting: Drowsiness detection systems typically incorporate real-time alerting mechanisms to warn drivers when signs of drowsiness are detected. Alerts can be auditory, visual (e.g., flashing lights or warnings on the dashboard), or haptic (e.g., seat vibrations), aiming to prompt the driver to take corrective action, such as pulling over for a rest or taking a break.

Published Paper   E-Certificate

Udhayakumar M, Sudhakaran R, Nishanth M, Priya V, & Tharun T (April-2024). DRIVER DROWSINESS DETECTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), g191-g196. https://ijnrd.org/papers/IJNRD2404620.pdf

Volume 9 Issue 4, April-2024

Pages : g191-g196

Paper Reg. ID: IJNRD_218954

Published Paper Id: IJNRD2404620

Downloads: 000121985

Research Area: Science & Technology

Country: Krishnagiri, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404620.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404620

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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