Enhancing Road Safety Through Cost-Effective, Real-Time Monitoring of Driver Awareness with Resource-Constrained IoT Devices

The prevalence of road and highway accidents, largely attributed to driver distraction, highlights the critical need for an intelligent system that can assess driver alertness and provide timely alerts. Current solutions in the market are often characterized by their high costs and complex installation processes, which significantly limit their accessibility and practical application on a broader scale. In response to this challenge, our research introduces a cost-effective, real-time framework designed to monitor driver alertness utilizing the Raspberry Pi, a device known for its limited processing capabilities. This inherent limitation prompted us to implement several optimizations, which are elaborated upon within our study, to equip the Raspberry Pi with the ability to make real-time decisions effectively. Our proposed approach features a novel algorithm that integrates Haar Cascade and facial landmark detection techniques, enabling the rapid and precise identification of facial features, thereby surpassing the accuracy of existing leading methods. This system meticulously tracks facial points to evaluate driver attentiveness through indicators such as drowsiness, yawning, and unusual facial movements. It utilizes metrics including the Eye Aspect Ratio (EAR), Lips Movement Ratio (LMR), and Face Position Difference (FPD) to initiate driver alerts, thereby contributing to the prevention of potential accidents. Furthermore, our system is enhanced with a GSM module, facilitating emergency notifications to the vehicle owner in critical situations. Extensive testing of our framework, involving participants of diverse sizes, skin colors, and ages, has demonstrated its efficacy in sustaining driver awareness with minimal processing delays, even when deployed on devices with limited computational resources. This affirms the potential of our proposed solution to serve as a viable and scalable option for enhancing road safety through improved driver alertness monitoring.