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ABSTRACT
Distractions are unpredictable and may lead to careless mishaps which are
difficult to prevent with population growth around the world. Researches have
been conducted numerous times to find a solution to prevent loss of life.
Passenger security is the main concern of the vehicle‘s designers and in order
to provide better security for saving lives of passengers, airbags are designed
but they do not prevent mishaps from happening. Distraction due to phone
notifications and general tiredness are the main causes. A wide range of
techniques based on behavioral measures using machine learning techniques
have been examined to scope out driver distraction in the past. The recent
growth of such technologies requires that these algorithms be improved to
evaluate their accuracy in identifying distraction. There are numerous features
of faces that are available to be extracted from any face to deduce the level of
distraction. These include eyes closed for longer than 5 seconds, head
movements and continuous yawning. However, the development of a shock
system to push out the distraction and immediately give an alert is a
challenging task as it requires accurate and robust algorithms. This study
takes a novel approach by using convolutional neural networks to scope out
the distraction and will immediately sound a loud siren to give a sensory
shock which brings back alertness. When paired with proper guidance, the
said hybrid approach would produce the best solution in real-time to such
issues in the future.
