DRIVER DROWSINESS DETECTION ALARM SYSTEM

DRIVER DROWSINESS DETECTION ALARM SYSTEM

Mr. Sachin Gajanan Chaudhari1,  Mr. Tirthraj Shivacharan Dhote2, Mr. Gaurav Kshirsagar Maraskole3, Mr. Sawan Anil Shende4, Asst. Prof. Akshay Juware5

1234Student,VIT, Nagpur

5 Asst. Prof.,VIT, Nagpur

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Abstract - Driver fatigue is a significant factor contributing to road accidents worldwide. To address this issue, we present the design and implementation of an Anti- Sleep Alarm System (ASAS) aimed at preventing accidents caused by drowsy driving. This project introduces a novel approach that combines real-time monitoring of driver's physiological signals with advanced signal processing techniques to detect signs of fatigue and trigger timely alerts. The ASAS utilizes non-intrusive sensors, such as electroencephalography (EEG) and electrooculography (EOG), to continuously monitor the driver's brainwave patterns and eye movements. These physiological signals are processed using machine learning algorithms to identify patterns associated with drowsiness and microsleep episodes. The system employs a wearable device integrated into a driver's headset, making it comfortable and convenient for extended use. The hardware architecture incorporates low-power microcontrollers and wireless communication modules, making the system energy-efficient and capable of real- time data transmission to a companion mobile application. The mobile app provides additional features such as real-time monitoring of the driver's physiological state, historical data analysis, and personalized fatigue risk assessment

Key Words:  Artificial Intelligence, Autonomous Vehicle Technology, Drowsiness Detection, Machine Learning.