Development of a System to Detect Drowsiness in Drivers through Artificial Intelligence Techniques to Prevent Traffic Accidents

Development of a System to Detect Drowsiness in Drivers through Artificial Intelligence Techniques to Prevent Traffic Accidents

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-1
Year of Publication : 2024
Author : Joel Leyva Meza, Jose Nuñez Liñan, Laberiano Andrade-Arenas
DOI : 10.14445/22315381/IJETT-V72I1P115

How to Cite?

Joel Leyva Meza, Jose Nuñez Liñan, Laberiano Andrade-Arenas, "Development of a System to Detect Drowsiness in Drivers through Artificial Intelligence Techniques to Prevent Traffic Accidents," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 155-163, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P115

Abstract
Road accidents usually have many causes, but one that is very common around the world is driver drowsiness. The development of an artificial intelligence-based driver drowsiness detection system has been proposed as a means to prevent these incidents and protect the lives of drivers and other road users. The main objective of this project is to develop a driver drowsiness detection system using artificial intelligence techniques capable of detecting drowsiness in real time and alerting the driver in order to prevent traffic accidents. This system will make it possible to warn drivers and take preventative action before an accident occurs. It will use an approach based on artificial intelligence techniques, in particular machine learning. This AI-based driver drowsiness detection system has the potential to significantly reduce the number of road accidents caused by drowsiness, thereby improving road safety and protecting human life. In order to carry out the development of this project, the XP (Extreme Programming) methodology was used in order to have better management and communication development among the working team.

Keywords
Artificial intelligence, Machine learning, Road safety, Drowsiness, XP methodology.

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