A Smart IoT-Based Framework for Adaptive Drowsiness Detection and Alert Mechanisms for Preventing Fatigue-Induced Accidents

Driver tiredness is a leading cause of traffic incidents, accounting for approximately 20-30% of deadly collisions globally. This highlights the urgent need for advanced monitoring and intervention technologies. This study presents a Smart IoT-based framework for Adaptive Drowsiness Detection and Alert Systems, designed to improve driver safety by identifying and preventing fatigue in real time. Achieving a 92.5% accuracy rate in detecting early signs of drowsiness, the system employs facial recognition cameras, physiological sensors, and IoT connectivity to continuously assess driver awareness. Capable of identifying fatigue patterns in just 2–3 seconds, the system triggers immediate alerts, including audio warnings ranging from 85–100 dB, seat vibrations exceeding 3G force, and dynamic lighting adjustments for timely responses. Through IoT integration, the framework supports cloud-based oversight, historical data review, and predictive analytics, empowering fleet operators and regulatory bodies to enhance road safety measures. Fatigue-related mistakes contribute to nearly 40% of crashes, and the development, deployment, and real-world evaluation of this system show it can reduce such incidents by as much as 90%.