Real-Time Decision System for Detecting Driver Intoxication

Driving under the influence (D VI) of alcohol is one of the primary causes of traffic accidents and has been a global concern. Numerous studies have been conducted over the years have focused on the implications of driving under the influence of alcohol [1], [2]. These studies have consistently revealed that alcohol significantly impairs a driver's cognitive and motor abilities. Specifically, alcohol slows reaction times, diminishes the ability to judge distances and speeds, impairs vision, and adversely affects decision-making. Moreover, intoxicated drivers tend to engage in riskier behaviors, such as speeding, neglecting to wear seat belts, weaving between lanes, and disregarding traffic signs and signals. In response to this critical public safety issue and the need to decrease alcohol-related accidents, we propose a real-time, self-operated system deployed on a Raspberry Pi platform that continuously monitors and analyzes the driver's facial expressions to detect intoxicated drivers (signs of drunkenness).