Abstract
Driver fatigue and distraction are major contributors to road accidents worldwide, especially in long-duration operations and rural areas. Current monitoring systems often rely on cellular connectivity to transmit alerts, limiting their use in regions without network coverage, such as mining routes in Peru. This research presents the design and validation of an intelligent system for real-time driver fatigue detection, integrating computer vision algorithms and visual biometric indicators with an autonomous communication channel. The system employs Python-based computer vision using Haar Cascade classifiers and the Eye Aspect Ratio (EAR) metric to detect ocular fatigue. Once an event is identified, an alert is transmitted via nRF24L01 radio-frequency modules, enabling communication without cellular infrastructure. The prototype was validated in simulated vehicular cabins, achieving 93% accuracy and reliable transmission up to 80 meters in open-field scenarios. The results confirm the feasibility of a cost-effective and scalable solution for enhancing road safety in contexts where conventional systems fail, with potential applications in mining transportation, public transit, and remote logistics.

Keywords
Driver fatigue, Computer vision, Biometric sensors, Autonomous communication, Road safety

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Mr. Jose Ignacio Soria