Effective Driver Fatigue Management and Prevention Using Cloud-Integrated RNN Models

Effective methods for managing and preventing driver tiredness are necessary since it is a significant factor in road accidents. In this research, it provides a new way to challenge this problem by integrating wearable devices with the cloud and using recurrent neural networks (RNNs). To detect signs of weariness, such as irregular heart rates and irregular eye movements, the system makes use of wearable sensors that can gather data in real-time. A cloud-based platform receives data sent by these sensors. To accurately identify and forecast weariness, RNNs are used to capture temporal relationships within the data streams. Personalized tiredness assessment, continuous monitoring, and prompt intervention via alerts or adaptive actions are some of the benefits offered by the proposed approach. The findings of the experiment show that the proposed method improves driver safety and traffic management by reliably detecting tiredness levels and reducing related hazards. This comprehensive system shows potential for extensive integration with automobiles, which would greatly enhance road safety initiatives.