SLEEPINESS OR DROWZINESS AND FATIGUE DETECTION SYSTEM OF DRIVER IN THE ROAD NETWORK

On the basis of data, it is analyzed that drivers feeling drowsy are responsible for accidents on the roads.[3], posing a serious threat to public safety. To tackle this issue and take advantage of the mobile devices, a unique solution for Driver Drowsiness or sleepiness and fatigue Detection[2] system has been proposed. This solution utilizes Convolutional Neural Networks along with Flask for image/live input. By following this two-step technique, the mobile devices in the car record and evaluate the driver’s current state while also ensuring their privacy. Then, boundaries verify sleepiness by comparing the data from the mobile client with the real-time input that was observed. The suggested framework uses a data fusion technique and is based on the distributed boundaries architecture, which ensures effective administration of the area of interest. This method uses flask manual input or real-time input of the car environment and the CNN model to identify driver fatigue locally based on facial expressions. With an impressive average accuracy, the framework’s sleepiness detection performance is remarkable.