Authors : Elijah L. Boon; Grazielle Nychole Dela Cruz; Chaz B. Honrada; Trishia Jane T. Javier; Paolo Roberto O. Lozada; Tommy A. Ditucalan

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/ms6djw4j

Scribd : https://tinyurl.com/4cwumntk

DOI : https://doi.org/10.38124/ijisrt/26feb752

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Driver fatigue accounts for approximately 10–20% of road accidents worldwide. This paper presents Lumina Alert, an AI-based in-vehicle driver drowsiness detection and intervention system deployed on a Toyota Avanza (2007). The system is implemented on a Jetson Orin Nano and uses a YOLOv11n-based facial monitoring model to detect drowsiness indicators such as eye closure and yawning. Upon detection, the system applies multi-modal interventions including auditory alerts, voice prompts, and ignition lockout. Experimental results show an overall detection accuracy of 93.1%, with performance exceeding 90% under occlusion conditions involving sunglasses and eyeglasses. The ignition lockout mechanism successfully prevented vehicle startup in 95% of drowsy cases, while auditory and voice alerts effectively refocused drivers in 92% of trials. The system achieved over 90% reliability in both daytime and nighttime conditions with a minimum response time of 7 ms, enabling real-time operation.

Keywords : Driver Drowsiness Detection; YOLO-Based Facial Monitoring; Embedded AI Systems; Ignition Lockout; Automated Interventions; Intelligent Vehicle Safety.

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