Abstract
In recent years, the casualties of traffic accidents caused by driving cars have been gradually increasing. In particular, there are more serious injuries and deaths than minor injuries, and the damage due to major accidents is increasing. In particular, heavy cargo trucks and high-speed bus accidents that occur during driving in the middle of the night have emerged as serious social problems. Therefore, in this study, a drowsiness prevention system was developed to prevent large-scale disasters caused by traffic accidents. In this study, machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. Additionally, a CO2 sensor chip was used to detect additional drowsiness. Speech recognition technology can also be used to apply Speech to Text (STT), allowing a driver to request their desired music or make a call to avoid drowsiness while driving.
Highlights
According to the statistics of Korea on traffic accidents in the last 5 years, driving while drowsy is one of the most important factors in traffic accidents, and its related mortality rate is more than 2 times higher than other causes of traffic accidents [1]
A device for preventing drowsy driving was selected, and interviews and surveys were conducted with operators who do a large amount of driving
The survey consisted of a total of 61 questions related to car driving and lifestyle, driving habits related to drowsy driving, the use of peripheral devices, the vehicle environment, accidents, and drowsy driving in order to gain insights to refine our ideas
Summary
According to the statistics of Korea on traffic accidents in the last 5 years, driving while drowsy is one of the most important factors in traffic accidents, and its related mortality rate is more than 2 times higher than other causes of traffic accidents [1]. As a solution to resolve these problems, it is possible to reduce the mortality rate of such traffic accidents by detecting and preventing drivers from driving while drowsy. Three techniques are used to detect the drowsiness of commercial drivers: recognizing the driver’s eyes through cameras and using biosignals such as breathing, temperature, and heart rate to analyze operation patterns, such as the abnormal use of pedals and steering wheels.
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