Description:Driver, tiredness, and drowsiness is the major factor that can involve serious property damage, serious accidents and can even result in deaths. Here The Driver Drowsiness detection system, can help the driver to know whether the state of his driving on road is safe or it can be dangerous due to the drowsiness.
[0003] The idea is to create a system which could generate Alarm message to the driver whether his/her state of driving is safe or not.This will not only be helpful to create a safe environment for the driver but for the passenger and for other fellow people on road.
[0004] US6822573B2: Two sleepiness detection subsystems that are connected to a control unit make up the drowsiness detection system. The control unit detects the driver's level of tiredness using sensory fusion, clever fuzzy algorithms, and the sensory data. The non-intrusive monitoring of the driver's various attributes by the system adds redundancy and boosts the accuracy of the system's sleepiness diagnosis.
[0005] EP1418082A1: The method for detecting drowsiness by keeping an
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eye on a person is made possible by the current innovation. A video imaging camera that can provide pictures of a person, including their eyes, is part of the system. The system also has a CPU for handling the pictures the video imaging camera produces. In order to identify whether the eye is open or closed, the CPU continuously scans the captured image. The processor also calculates the percentage of a time interval during which the eye is closed, and when this percentage surpasses a threshold number, it defines a state known as sleepiness.
[0006] US8743193B2 A drowsiness model is given to determine a value describing the driver's tiredness as a function of at least one output quantity of a driver-activity sensor array in a method and a device for detecting drowsiness of a driver in a motor vehicle. The device for detecting sleepiness also has a brightness sensor and a correction model for correcting the value describing the driver's tiredness as a function of at least one brightness sensor output quantity.
[0007] INA202341015300 A machine learning model is used in the design and development of the driver's drowsiness detection system. In this article, we discuss an online voting system that enables participation in online voting by the voter, the candidate, and the administrator (who will be in charge of and check all the user and information). Our online voting system is extremely safe and features a user-friendly, engaging design. The suggested online portal is safe and has special security features like unique ID creation that offer an additional layer of protection (apart from
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login id and password) and allow admin the power to verify the user information and determine whether or not he is qualified to vote. As all users must log in, it also produces and handles voting and election details.
[0008] Drowsiness while driving is a leading cause of road accidents worldwide. To mitigate this risk, researchers and engineers have developed various driver drowsiness detection systems using machine learning techniques. These systems leverage various sensors such as eye-tracking cameras, steering wheel sensors, and accelerometers to detect and analyze the driver's behavior and physiological signals to estimate the level of drowsiness accurately. These systems use machine learning algorithms such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to classify the driver's level of drowsiness accurately. Such systems are increasingly becoming essential features in modern vehicles, enabling the accurate analysis of driver alertness, and preventing accidents caused by drowsiness on the road.
[0009] Driver Drowsiness detection system will give the signal message to the driver that he is drowsy and should take rest or stop driving for now. , Claims:In case of a driver, tiredness and drowsiness is the major factor that can involve serious property damage, serious accidents and can even result in deaths.
2. The idea is to create a system which could inform the driver that the physical state in which he is driving the vehicle is Active and safe or drowsy and dangerous.
3. This will help to reduce on road accident that are caused by the drowsiness of the driver and could not just safe life of the driver but of the fellow passengers and properties
4. These systems use various sensors and machine learning algorithms to collect and analyze data and generate alerts or warnings to the driver when the level of drowsiness reaches a specific threshold