Publication Type:

Conference Proceedings


Advances in Distributed Computing and Machine Learning, Springer Singapore, Singapore (2021)





Despite the sophisticated technology that could prevent accidents of vehicles on highways, many lives are claimed due to the drowsiness of drivers. According to the data reported by the NHTSA (National Highway Traffic Safety Administration) of USA, 846 succumb to death and it has become a major threat these days which is evident from 83,000 cases registered due to drowsy driving. Drowsiness is the feeling of being sleepy or being inactive towards activities and this causes a sleeping sensation which leads to closure of eyelids while driving resulting in major accidents. There are systems in action that could detect physical awaken of the driver, but detecting the drowsiness and deviation of the driver is a challenging problem in the field of transportation. Many factors could influence the drivers to fall asleep during their journeys and the chances of getting drowsy increases in night times than in the dawn and journeys did alone are even more dangerous. So, we introduce a system which is capable of monitoring the person's consciousness, acceleration pattern and the angle of vehicle's steering simultaneously to detect the deviation and drowsiness of the driver. The process of drowsiness and deviation detection of the driver has been done using image processing techniques. Our proposed system alerts the driver by comparing the acceleration pattern to the Eye Aspect Ratio (EAR) for drowsiness. It also monitors his facial movements, changes in steering angle to detect the deviation.

Cite this Research Publication

S. K. Vasudevan, Anudeep, J., Kowshik, G., and Prashant R. Nair, “An AI Approach for Real-Time Driver Drowsiness Detection–-A Novel Attempt with High Accuracy”, Advances in Distributed Computing and Machine Learning. Springer Singapore, Singapore, 2021.