Abstract
ABSTRACT Absence of forbearance among drivers, fatigue and irresponsible behaviour among drivers result in countless fatal crashes and road traffic injuries. Driver drowsiness is a highly problematic issue which impairs judgment and decision making among drivers resulting in fatal motor crashes. This paper describes a simple drowsiness detection approach for a smartphone with Android application using Android Studio 3.6.1 and Mobile Vision API for drowsiness detection before and while driving. Physiological analysis and a quick facial analysis were performed to check drowsiness before the driver starts driving. The smartphone camera was used for analysing the heart rate by tracking colour changes due to blood flow on the fingertip. Facial analysis was undertaken by Google Vision API which determined the head position, blinking duration and yawning frequency through the eye opening and mouth opening probabilities. The heart rate, blinking duration, yawning frequency and speeding were used as indicators for drowsiness. The facial analysis was repeated with speeding data while driving with results analysed each one minute. A performance accuracy of the combined results with speeding detection proved to be around 93.3%. Additional keywords: Drowsiness detection; Facial analysis; Heartrate; Mobile Vision API; Physiological analysis.
Highlights
Drowsiness is one of the significant reasons for road crashes that results in considerable damaging consequences to the individuals who suffer fatal or non-fatal injuries, property damage and economic losses to the nation
A survey conducted by the National Highway Traffic Safety Administration (NHTSA) estimated that 8.8% to 9.5% crashes resulted from road accidents in 2018 in the United States [1]
Another study made by the American Automobile Association (AAA) Foundation for Traffic Safety identified that around 328,000 drowsy driving crashes occur yearly [2]
Summary
Drowsiness is one of the significant reasons for road crashes that results in considerable damaging consequences to the individuals who suffer fatal or non-fatal injuries, property damage and economic losses to the nation. Another study made by the American Automobile Association (AAA) Foundation for Traffic Safety identified that around 328,000 drowsy driving crashes occur yearly [2]. Common symptoms that have been identified during drowsy driving include: constant nodding, difficulty opening eyes, missing road signs and turns, frequent lane drifting and difficulty in maintaining speed [3]. A highly accurate system aims at identifying dangerous vehicle manoeuvres by a drunk driver and alerting the driver based on sensor readings or calling the police before any accident occurs [8]. A model was proposed that brought a distinctive and innovative approach such that drowsy detection was predicted using lane heading difference metric alongside fatigue measures including driver reaction time and oculomotor movement [11]. The vehicle heading metric recorded the difference between the direction of the vehicle and the tangential direction of the vehicle, both measured in degrees
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