A Survey on Driver Drowsiness Detection Techniques
Reshma J1, Aishwarya B2, Farheen Khanam Z M2, G Sai Vennela2, Lekhana A V2
1Associate Professor, Department of Computer Science & Engineering, B N M Institute of Technology, Bangalore, India
2Student, Department of Computer Science & Engineering, B N M Institute of Technology, Bangalore, India
Abstract— The developments in technology over the years bring the support to drivers using smart vehicle systems. In past few years, there has been substantial increase in road accidents in India and worldwide as well. The most significant reasons for the same are drowsiness and fatigue. Therefore, driver drowsiness and fatigue detection is major possible area to prevent a large number of sleep induced road accidents. Considering this problem, this article proposes different methods for Driver Drowsiness Detection System applicable in motor vehicles. The system employed various applications using blink rate, eye closure and yawning to effectively and quickly identify the drowsiness of a driver while driving the vehicle and alarm the driver accordingly.
Keywords— Drowsiness Detection, Prevention, Road Accidents, Eye State, Alarm
I. INTRODUCTION
Driving fatigue is a common phenomenon due to long time of driving or lack of sleep. It is a significant potential hazard in traffic safety. As many as 100,000 traffic accidents caused by driving fatigue which led to 400,000 people injured and 1550 people killed happened in the United States each year [1].
Driver Drowsiness Detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads. Some of the current systems learn driver patterns and can detect when a driver is becoming drowsy. Various technologies can be used to try to detect driver drowsiness.
1) Steering pattern monitoring- Primarily uses steering input from electric power steering system. Monitoring a driver this way only works as long a driver actually steers a vehicle actively instead of an automatic lane-keeping system.
2) Vehicle position in lane monitoring- Uses lane monitoring camera. Monitoring a driver this way only works as long as a driver actually steers a vehicle actively instead of an automatic lane-keeping system.
3) Driver eye/face monitoring- Uses computer vision to observe the driver’s face, either using a built-in camera or on mobile devices.
4) Physiological measurement- Requires body sensors to measure parameters like brain activity, heart rate, skin conductance, muscle activity [2]