AI-Powered Edge Vision Traffic Management Platform for Real Time Monitoring and Congestion Reduction
DOI:
https://doi.org/10.70914/Keywords:
EAR,, Raspberry Pi,, Eye Detection, and Driving Behavior Analysis.Abstract
Drowsiness and fatigue are among the top factors that are considered to contribute to car accidents,
causing 1.3 million deaths per year. By using facial landmark identification, the Advanced Drowsiness identification
System is able to accomplish its goal of lowering the total number of road accidents caused by various forms of
driver weariness and falling asleep behind the wheel. Using a facial recognition algorithm, this system is able to
identify signs of sleepiness in a picture. It detects and tracks the driver’s face and eyes to compute the Eye Aspect
Ratio (EAR), a validated measure for drowsiness detection. This system reduces fatalities and improves road safety
by detecting driver drowsiness using Driving Behaviour Analysis (DBA). The goal of this technology is to make
transportation safer by reducing the likelihood of driving errors caused by driver sleepiness.
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