Given that commuter traffic accidents can result in serious injuries or even fatalities, preventing them is imperative to preserving traffic safety. Due to its effects on the driver's inability to make prompt decisions, increased reaction time, and decreased attention, driver sleepiness is a major causative factor to auto mishaps. To reduce the frequency of accidents, we put forward a scheme that will find out if the motorist is feeling exhausted or lethargic by recording certain body parameters like eyes and lips shapes and positions. Our proposed system tracks the driver's lip movements, eye movements, and flash rate in real time to detect motorist is feeling sleepy or not. The proposed system consists of an inbuilt camera on the car that records the motorist facial expressions and transfers the recording to a computer for further processing. Machine learning (ML) models trained and tested on large datasets are used for finding out if the motorist is feeling sleepy or not. The ML models are trained to find out if the motorist is feeling exhausted or not by observing the body images specially the eyes and lips areas. If the driver becomes too sleepy, the technology is meant to alert both the driver and the vehicle's control system. The system will alert the driver to take necessary safety measures to avoid road accidents. They can also be auditory or visible signals. The advantages of the system are improved traffic safety, a decline in the frequency of crashes caused by fatigued drivers, and a reduction in the severity of accidents that do occur. Owing to the system's important insights regarding driving behavior, vehicle manufacturers and organizations in charge of road safety may be able to develop better systems that are more driver-focused. The suggested approach is also applicable in other fields, including railways and aeronautics. Finally, using the lip aspect ratio (LAR), eye aspect ratio (EAR), lip, and eye recognition algorithms, our research aims to develop a instantaneous motorist tiredness recognition system. The device could greatly increase traffic safety and lower the amount of accidents caused by intoxicated drivers.
Automatic Drowsiness Detection to Minimize Road Accident
Lect. Notes Electrical Eng.
International Conference on Information Technology ; 2024 ; Noida, India March 06, 2024 - March 07, 2024
14.02.2025
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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