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Feeling sleepy while driving is one of the leading
reasons behind road accidents around the world. That’s why
detecting drowsiness early is so important for keeping people safe
on the road. This paper takes a closer look at different ways to spot
drowsiness in drivers, including methods based on computer
vision, body signals, machine learning, and driving simulations.
Among these, computer vision techniques focus on tracking facial
features like blinking, yawning, and head movements to spot signs
of tiredness. Physiological signal-based techniques include
Electrooculogram, and heart rate variability, which are very
accurate but intrusive and complex for real-world applications.
Approaches from machine learning and deep learning,
Convolutional Neural Network, demonstrate strong promise
through real-time prediction that combines visual and behavioral
indicators. Systems for detection are improved with the
incorporation of simulation-based systems with signals derived
from both vehicle-based and driver-monitoring ones within
controlled environments
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Nihit Jain
Jivika Kashyap
Nikhil Sharma
Nand Kishor Yadav