International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
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Real Time Driver Drowsiness Detection Hybrid Approach Praveen Kumar S1, Naveena K N2, Saritha A N3, Sushma T P4, Vedhavathi N L5 1,2,4,5 Undergraduate Student, B.M.S. College of Engineering, Bengaluru, India 3Assistant Professor, B.M.S. College of Engineering, Bengaluru, India
----------------------------------------------------------------------------***----------------------------------------------------------------------without steering input. The technology can detect these Abstract - Driver drowsiness detection is an patterns, the system can infer when the driver is becoming drowsy and issue an alarm.
important safety feature in modern vehicles, as it can help prevent accidents occurred by fatigue or falling sleep while driving. A hybrid approach to detecting driver drowsiness combines multiple methods to improve accuracy and reliability. One common method for detecting drowsiness is through the use of visual tracking systems, which use cameras to track the driver's eyes and facial expressions. Another method is through the use of physiological sensors, such as electrocardiography (ECG) or using pulse sensor, which will measure heart rate to detect changes indicative of drowsiness. A hybrid approach combines these methods, using the strengths of each to give a more reliable results of the driver's alertness. The visual tracking system can detect signs of drowsiness such as eye closure or nodding, while the physiological sensors can measure changes in brain activity or heart rate that may indicate drowsiness. This hybrid approach has the potential to improve the accuracy and reliability of drowsiness detection systems, helping to keep drivers and passengers safe on the road.
A hybrid approach incorporates multiple methods, such as using both physiological sensors and machine learning algorithms, to improve precision and reliability of the drowsiness detection system. This can be particularly useful in instances where one method may be less successful on its own, such as when a driver is wearing sunglasses or has a medical condition that affects their eye movement. Real-time driver drowsiness detection systems can be integrated into a vehicle's existing monitoring and warning systems, or they can be installed as standalone devices. They can be used to alert the driver to the need for a break or to pull over and rest, or they can trigger alarms or alerts for other drivers on the road. In some cases, the system may even be able to automatically slow or stop the vehicle if the driver does not respond to the alert. Overall, real-time drowsiness related automobile accidents have the potential to significantly reduce the number of accidents and fatalities caused by driver fatigue. By alerting drivers to the need to rest and take a break, these systems can help keep everyone on the road safer.
I. INTRODUCTION Driver fatigue is a major contributor to road accidents and traffic fatalities, with estimates suggesting that it may be a factor in up to 20% of all crashes. As a result, there has been a increase interest in developing drowsiness detection systems to detect when a driver is becoming drowsy and alert them to the need to rest or pull over.
II. PROBLEM STATEMENT Since drowsy driving can be particularly dangerous because it can affect a driver's response time, judgment, and decision-making ability, making it more difficult for them to respond to changing road conditions or avoid potential risks. It can also raise the risk of lane deviations, sudden braking, and other unpredictable driving behaviors that can put other drivers at risk.
One approach to driver drowsiness detection is using sensors to track physiological signs such as eye movement, blinking rate, and facial muscle activity. For instance, a system may use a camera to track the position and movement of the driver's eyes, or employ sensors to measure changes in skin conductance or heart rate. By analyzing these indicators, the system can identify when the driver is about to go in drowsy state and issue an alert.
The goal of driver drowsiness detection hybrid approach is to seek to identify when a driver is becoming drowsy or exhasuted and alert them to the need to rest or pull over, in order to reduce the risk of accidents caused by driver fatigue. By combining multiple methods, such as using both physiological sensors and machine learning algorithms, the system can improve the precision and
Another strategy is to use machine learning algorithms to analyze patterns of vehicle movement, such as lane deviations, abrupt braking, or extended periods of time
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