International Journal of Advanced Innovative Technology in Engineering (IJAITE)

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Real-Time Night Vision Drowsiness Detection Using Machine Learning

Dr. Prof. M. S. Khatib Samiksha Bagde , Diksha Jawade, Pratik Dongre, Bushra Khan, Divya Kawale

Abstract :

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the drivers attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, this paper proposes two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes movements using an MSR plus a simple heuristic. This component issue alerts the driver when the driver’s eyes show distraction and are closed for a longer than usual duration.

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