INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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.
"DRIVER DROWSINESS DETECTION", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.g191-g196, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404620.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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