International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
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A Survey on Driver Drowsiness Prediction System Using Machine Learning Dr Kalli Srinivasa Nageswara Prasad1, Yatham Zeeharika2, Vempati Reshmitha3, Siram Naga Tulasi Sriram4, Tangella Pujitha Ramya5 1Associate Professor, Dept. of Computer Science and Engineering, Sasi Institute of Technology & Engineering,
Tadepalligudem, A.P., India
2,3,4,5 Students of Computer Science and Engineering, Sasi Institute of Technology & Engineering, Tadepalligudem,
A.P., India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The vast majority of accidents nowadays have
Driver drowsiness detection is a term used to describe a device or system that tracks a driver's level of alertness and looks for indicators of exhaustion or drowsiness while they are driving. The driver's behaviour is often studied using a variety of sensors and algorithms, including steering patterns, eye movements, facial expressions, and occasionally physiological indications like heart rate. These systems' main objective is to warn the driver when they exhibit signs of being too fatigued to operate the vehicle safely, hence lowering the possibility of accidents brought on by sleepy driving. Driver Drowsy driving can be just as deadly as drunk driving, hence drowsiness monitoring devices are a crucial safety element. They can warn fatigued drivers about the need for rest, which can help prevent accidents and save lives.
been caused by driver fatigue for many years. Numerous road collisions are caused by drowsy driving. Despite the development of various sleepy systems over the past decade, existing systems still require improvement in efficiency, accuracy, cost, speed, and availability. Accidents brought on by fatigue and lack of sleep frequently involve drowsiness. In an effort to lessen these collisions, driver sleepiness detecting devices were developed. The passengers' safety can be ensured using this method in real-time systems. Images are captured in this using a webcam. Deep learning techniques were employed to analyze photos and extract information about a driver's facial expressions, eye movements, and head position. A camera records human images, and research is being done to see how that information can be applied to raise driving safety. This method uses a dataset of actual driving situations to show how well it can identify drowsy drivers. Gather photos from a live camera feed, run a machine learning algorithm on the image to check whether or the driver is sleepy or not. The input image is classified as drowsy or not using the machine learning technique. Finally, this study will handle several difficulties at once based on a variety of characteristics and will give a thorough method for anticipating driver fatigue. Key Words: Driver drowsiness, Accident, Facial Expression, Fatigue detection, Eye and mouth tracking.
The creation of a sleepiness detection system makes use of a camera that captures a live video of the driver's face. This technology continuously scans the driver's eyes and facial features for indicators of tiredness. It specifically looks to see if the motorist has open or closed eyes. If drowsiness is detected, the driver receives a warning signal. The system calculates the proportion of time the eyes remain closed for a specific duration. The technology determines that the driver is dozing off if the cumulative eye closure time exceeds a certain threshold and sounds an alarm to warn them.
1.INTRODUCTION
2.RELATED WORK
Due to driver drowsiness many accidents were happening. Driver drowsiness increases the risk of accidents and collisions, as fatigued drivers experience impaired reaction times and compromised judgment, making them more prone to collisions. When drivers are tired, they can't react quickly, make bad decisions, and even fall asleep briefly while driving. This can lead to accidents and makes our roads less safe. Additionally, sleepy driving puts the safety of other road users, pedestrians, and passengers at risk in addition to the driver. To avoid these issues and maintain everyone's safety on the roadways, it is imperative that everyone obtain enough rest before getting behind the wheel.
Existing approaches for detecting driver fatigue frequently concentrate exclusively on one or two aspects of driving behaviour, such as eyelid closure or steering wheel movements, according to V. Uma Maheswari et al. [1]. For detecting driver drowsiness, a number of technologies are used, such as PERCLOS, speech processing data, and linear regression. While these techniques have shown some promise in terms of identifying sleepiness, their applicability and accuracy are frequently constrained. The suggested method analyses several characteristics of driver behaviour and environment using image processing techniques. There are ways to use hybrid machine learning to identify and detect driver tiredness at an early stage, according to a report [2]. The proposed process is discussed, which calls for a camera to record the driver's footage and separate it into
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