• Varshini K Shetty
• S Samhitha
• Sushma N
• Ashwini S Savanth

According to Global status report on road safety 2018, launched by WHO, vehicle safety is increasingly critical to the prevention of crashes and has been shown to contribute to substantial reductions in the number of deaths and serious injuries resulting from road traffic crashes. Our proposed system comprises features such as warning the driver of fatigue using real-time non-intrusive concepts, electronic stability control, and automatic braking of the vehicle that can prevent a crash from occurring or reduce the severity of injuries. We make use of the small security camera to capture the drivers face and then employ the facial landmark algorithm which localizes and represent salient regions of the face, in this case, the eyes. An accelerometer is employed as a crash detector of the vehicle before, during, or after a crash. An alcohol sensor is made use of to detect the alcohol content in human breath. Distance measurement of any obstacle in the path of the vehicle which may be stationary or moving is done using the ultrasonic sensor. As the automation of automotive technology advances rapidly and driver assistance systems quickly become the new standard in day-to-day driving and we will soon be relying on these systems to ensure our safety on the road.

Accidents, Drowsiness, Driver Assistance System, Raspberry Pi, Arduino Uno

"Driver Assisting Smart Vehicle System Using Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.361-365, June-2020, Available :http://www.jetir.org/papers/JETIR2006392.pdf

"Driver Assisting Smart Vehicle System Using Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp361-365, June-2020, Available at : http://www.jetir.org/papers/JETIR2006392.pdf