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 |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-1 |
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| Year of Publication : 2024 | ||
| Author : Joel Leyva Meza, Jose Nuñez Liñan, Laberiano Andrade-Arenas |
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| 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.
References
[1] Lara Lévano Edwin, “Facial Gesture Recognition System Captured through Cameras to Analyze the Level of Customer Satisfaction in Restaurants,” Interfaces, pp. 1-26, 2019.
[Google Scholar] [Publisher Link]
[2] Ñiguez Hernández Adrián, “Drowsiness Detection for Accident-Free Driving,” University of Alicante, pp. 1-49, 2022.
[Google Scholar] [Publisher Link]
[3] Suarez Buitrago, Eduard Antonio, “Drowsiness Detection System in Car Drivers Using Image Processing Techniques and Machine Learning,” University of Pamplona – Faculty of Engineering and Architecture, pp. 1-95, 2022.
[Google Scholar] [Publisher Link]
[4] Anthony D. McDonald et al., “Steering in a Random Forest: Ensemble Learning for Detecting Drowsiness-Related Lane Departure,” Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 56, no. 5, pp. 986-998, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[5] A. Caso, J. Rey de Castro, and E. Rosales-Mayor, “Sleep Habits and Traffic Accidents in Inter-Provincial Bus Drivers of Arequipa, Peru,” Peruvian Journal of Experimental Medicine and Public Health, vol. 31, no. 4, pp. 707-711, 2014.
[Google Scholar] [Publisher Link]
[6] Gonzalez Dominguez, and Eros Sting, “Sociodemographic and Economic Factors Associated with Mortality from Traffic Accidents in South American Countries: An Ecological Study,” Cesar Vallejo University, pp. 1-37, 2023.
[Google Scholar] [Publisher Link]
[7] Elena Magán et al., “Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images,” Applied Sciences, vol. 12, no. 3, pp. 1-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mohammad Hijji et al., “FADS: An Intelligent Fatigue and Age Detection System,” Mathematics, vol. 11, no. 5, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Alexandru Burlacu et al., “Accurate and Early Detection of Sleepiness, Fatigue and Stress Levels in Drivers through Heart Rate Variability Parameters: A Systematic Review,” Reviews in Cardiovascular Medicine, vol. 22, no. 3, pp. 845-852, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] V. Venkatramaphanikumar Sistla et al., “Stacked Ensemble Classification Based Real-Time Driver Drowsiness Detection,” International Journal of Safety and Security Engineering, vol. 10, no. 3, pp. 365-371, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Faisal Dharma Adhinata, Diovianto Putra Rakhmadani, and Danur Wijayanto, “Fatigue Detection on Face Image Using FaceNet Algorithm and K-Nearest Neighbor Classifier,” Journal of Information Systems Engineering and Business Intelligence, vol. 7, no. 1, pp. 22-30, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Yauri Machaca, and Melissa Rosemery, “Vehicle Driver Drowsiness Detection System through Image Processing Using Matlab,” University of Sciences and Humanities, pp. 1-133, 2021.
[Google Scholar] [Publisher Link]
[13] Coaquira Coaquira, Guimer Senon Ramirez Ticona, and Orestes, “System to Detect the State of Drowsiness of Vehicle Drivers Using Artificial Vision in the City of Tacna,” Private University of Tacna, pp. 1-152, 2022.
[Google Scholar] [Publisher Link]
[14] Seok-Woo Jang, and Byeongtae Ahn, “Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT,” Sustainability, vol. 12, no. 7, pp. 1-13, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Limaquispe Miguel Raúl, and Mayon Sanchez Emerson, “Sleepy Detection System through Artificial Intelligence in Vehicle Drivers to Alert the Occurrence of Traffic Accidents,” National University of Huancavelica, pp. 1-82, 2018.
[Google Scholar] [Publisher Link]
[16] Sayon Karmakar, and Seshadri Mohan, “An IoT-Based System with Machine Learning for Detecting Drowsiness of Drivers,” Journal of Mobile Multimedia, vol. 17, no. 1-3, pp. 311-328, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Díaz Román, and José David, “Drowsiness Detection in Vehicle Drivers through Video Processing,” Institute of Engineering and Technology, vol. 43, no. 141, pp. 700-721, 2020.
[Google Scholar] [Publisher Link]
[18] Anang Kurniawan, and Fauziah, “BRI's Internal SIMPEL (Training Management Information System) Uses Agile Methods with Extreme Programming Models and Brute Force Algorithms,” Journal of Information and Communication Technology, vol. 7, no. 2, pp. 270-279, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Edwin Bautista-Villegas, “Agile XP and Scrum Methodologies, Used for the Development of Web Pages, under MVC, with PHP Language and Laravel Framework,” Amazonía Digital Magazine, vol. 1, no. 1, pp. 1-7, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Astucuri Inca, and William Teófilo, “Web System Using the XP Methodology for Order Management in the Forij Glass Company,” Autonomous University of Peru, pp. 1-147, 2019.
[Google Scholar] [Publisher Link]
[21] Dwi Wulandari Sari, and Kurnia Gusti Ayu, “Developing BuJel Application Using Extreme Programming (XP) Methodology,” International Journal of Computer Techniques, vol. 8, no. 2, pp. 265-272, 2021.
[Google Scholar] [Publisher Link]
[22] Mamani Diaz, and Rafael Angel, “Implementation of a Web System Using the XP Methodology for Incident Management in the Development Area of the Company TEMPUTRONIC S.A.C.,” National Technological University of Lima South, pp. 1-131, 2019.
[Google Scholar] [Publisher Link]
[23] Jimmy Molina-Ríos, and Nieves Pedreira-Souto, “Comparison of Development Methodologies in Web Applications,” Information and Software Technology, vol. 119, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Seungeun Chung et al., Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning,” Sensors, vol. 19, no. 7, pp. 1-20, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Mochamad Jejen Jaelani et al., “Website Based Student Absence Application Design Using Extreme Programming Method: Study at a University in Bandung,” Journal of Computech and Business, vol. 17, no. 1, pp. 9-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Rohmat Indra Borman, Adhie Thyo Priandika, and Arif Rahman Edison, “Implementation of the Extreme Programming (XP) System Development Method in Livestock Investment Applications,” Journal of Information Systems and Technology, vol. 8, no. 3, pp. 272-277, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Chalco Zacarias, Johnjahiro Jordan, “Information System Based on the XP Methodology for Information Technology Inventory Control,” Peruvian University Los Andes, pp. 1-149, 2019.
[Google Scholar] [Publisher Link]