  Around 40 percent of accidents related to driving on highways in India occur
due to the driver falling asleep behind the steering wheel. Several types of
research are ongoing to detect driver drowsiness but they suffer from the
complexity and cost of the models. In this paper, SleepyWheels a revolutionary
method that uses a lightweight neural network in conjunction with facial
landmark identification is proposed to identify driver fatigue in real time.
SleepyWheels is successful in a wide range of test scenarios, including the
lack of facial characteristics while covering the eye or mouth, the drivers
varying skin tones, camera placements, and observational angles. It can work
well when emulated to real time systems. SleepyWheels utilized EfficientNetV2
and a facial landmark detector for identifying drowsiness detection. The model
is trained on a specially created dataset on driver sleepiness and it achieves
an accuracy of 97 percent. The model is lightweight hence it can be further
deployed as a mobile application for various platforms.
