AI Safety Guard: Design, Prototype Implementation, and Validation Roadmap for a Privacy-Preserving Multi-Sensory Edge-AI Driver Drowsiness System

Abstract

Driver drowsiness is a persistent road -safety problem whose episodic and under -reported nature complicates both prevention and measurement. This paper presents AI Safety Guard, a low -cost edge- AI prototype that combines non -contact facial-landmark analysis with bounded auditory and optional olfactory alerts. The proposed artefact uses local camera processing to estimate sustained eye closure, mouth opening and yawn patterns, and head -pose deviation; temporal decision fusion then triggers an active warning through a speaker or buzzer and, when enabled, a short, atomised scent pulse. Unlike cloud-dependent monitoring, the prototype is designed to retain no video and to record only minimal local event information. The study adopts a design -science and safety -by-design methodology: it reconstructs system requirements, specifies the hardware and inference architecture, formalises the tri- channel decision logic, and evaluates the credibility and limits of preliminary prototype evidence. Project documentation reports operation on Raspberry Pi-class hardware at approximately 10-15 frames per second, local event logging, hard -coded ac tuator duration, cooldown lockout, manual acknowledgement, and a scent opt-out. A website event trace reports 116 ms from a detection event to alert activation, whereas a separate pitch document claims 0.001 s actuation latency; this discrepancy is treated as an unresolved measurement issue rather than evidence of validated performance. The paper therefore distinguishes artefact feasibility from safety efficacy. It proposes a five -phase validation programme covering bench metrology, public -dataset evaluation, simulator experiments, closed -track trials, and regulatory readiness against functional-safety, safety-of-the-intended-functionality, privacy, and human -machine-interface requirements. The principal con tribution is an evidence -bounded blueprint for translating a student -developed prototype into a testable driver -monitoring system while preserving privacy and explicitly managing intervention risk. The system is not positioned as a substitute for sleep, rest, or safe pull-over behaviour, but as a supplementary warning device requiring independent validation before road deployment.