AI-Based Driver Drowsiness Detection System
Computer Science
This project aims to prevent road accidents by detecting driver fatigue using AI and computer vision.
The proposed project utilizes artificial intelligence (AI) and computer vision techniques to detect driver fatigue, thereby preventing road accidents. The system employs a multi-modal approach, combining various computer vision and machine learning algorithms to accurately detect and alert drivers when they exhibit signs of fatigue.The system consists of several key components, including a camera, sensor suite, and edge computing device. The camera captures video feed of the driver's face, which is then processed by the computer vision algorithm to detect facial features, track eye movements, and analyze head poses. The sensor suite collects additional data, such as driver interaction with the vehicle (e.g., steering wheel movements, pedal pressure), vehicle speed, and environmental factors (e.g., temperature, humidity).The AI-powered algorithm analyzes the collected data, applying techniques such as deep learning, machine learning, and image processing to identify patterns indicative of driver fatigue. These patterns may include, but are not limited to, excessive blinking, yawning, or nodding of the head. The algorithm also considers contextual factors, such as the time of day, driving duration, and road conditions, to improve detection accuracy.Upon detecting driver fatigue, the system triggers an alert, which can be in the form of visual, auditory, or haptic feedback. The alert is designed to rouse the driver and prevent the vehicle from drifting out of its lane or entering a state of uncontrolled motion. The system can also integrate with existing vehicle safety features, such as lane departure warning systems and adaptive cruise control, to enhance overall safety.The technical implementation of the system involves several steps: (1) data collection and annotation, (2) model training and validation, (3) edge computing device integration, and (4) testing and evaluation. The system will be trained on a large dataset of driver images and sensor readings, annotated with labels indicating the presence or absence of fatigue. The trained model will be deployed on an edge computing device, which will process the video feed and sensor data in real-time, generating alerts as needed.The system's performance will be evaluated using metrics such as accuracy, precision, recall, and F1-score, with respect to detecting driver fatigue. The system will also undergo rigorous testing, including simulation-based testing, driver study testing, and on-road testing, to ensure its reliability, robustness, and effectiveness in real-world scenarios.The proposed system has the potential to significantly reduce the number of road accidents caused by driver fatigue, saving lives and preventing injuries. By leveraging AI and computer vision techniques, the system offers a proactive approach to road safety, providing an additional layer of protection for drivers, passengers, and other road users.
This project aims to prevent road accidents by detecting driver fatigue using AI and computer vision. A camera continuously monitors facial expressions and eye movements to detect signs of drowsiness, triggering alarms or automatic braking when needed.
United Nations Organization (UN)
Artificial intelligence, Software
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