IoT-Fog-based framework to prevent vehicle–road accidents caused by self-visual distracted drivers

An increasing number of road accidents, a major global issue, are linked to growing demands for faster vehicle speeds. Analytical research highlights the significance of self-visual distraction events for drivers in these occurrences. In this study, we present an IoT-Fog-based framework that is intended to automatically identify and notify drivers of self-visual distraction as it occurs. With a conditional evaluation dependent on the status of the driver, the proposed framework uses Convolutional Neural Networks (CNNs) to extract characteristics and expressions indicative of a distracted state of the driver. The framework shows encouraging results, proving the effectiveness of the model with a high accuracy rate of 90% and precision value of 91%. Notably, in terms of detection accuracy, the suggested framework outperforms other state-of-the-art techniques. Real-time alerts that involve prompt recognition and persistent beeping until normality is restored can reduce traffic accidents and are an essential intervention for saving lives.