Real Time Prevention of Driver Fatigue and Exhaustion Using Deep Learning

Real Time Prevention of Driver Fatigue and Exhaustion Using Deep Learning

Prof. Ankita Kotalwar1, Parth Dongare2, Mushir Inamdar3, Yash Dhomse4, Samruddhi Gadekar5

*1 Assistant Professor, Department of Computer Engineering, Sinhgad College of Engineering, Vadgaon, Pune, Maharashtra, India

*2,3,4,5 Department of Computer Engineering, Sinhgad College of Engineering-Vadgaon, Pune, Maharashtra, India

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1. ABSTRACT

Car accidents are frequently caused due to many reasons like drowsiness, drunkenness or tiredness, which has serious consequences for traffic safety. If advanced warning systems were in place to alert drowsy drivers, it could potentially prevent numerous fatal accidents. There are a number of drowsiness detection technologies that can be used to check for signs of inattention while driving and alert the driver. Self-driving cars must have sensors that can tell if a driver is drowsy, irritated, or going through dramatic swings in their emotions. So far, various additional alert systems have been proposed to help prevent drowsy driving. These systems sound an alarm when they notice a major lane departure, a change in the headway distance, or other symptoms of poor driving performance. Other warning signs include increased head wobble or prolonged eye closure duration. These systems typically identify such behaviour after sleepy driving has started. This piece explores numerous ways in which Driver Drowsiness Detection Systems can be applied in automobiles to address this particular concern.

Keywords: Drowsiness-Detection, Driver-Fatigue, Computer Vision, Real Time, Survey