Real-Time Driver Drowsiness Detection with Machine Learning

DOI

10.2991/978-94-6463-738-0_60How to use a DOI?

Keywords

Driver Drowsiness Detection; Machine Learning; Face Detection; Fatigue Monitoring; Real-time Alert System

Abstract

In this paper, a machine learning-based system for detecting vehicle safety is presented. The primary objective is to monitor and detect driver drowsiness in real time using face detection techniques. The main aim of this project is to identify signs of drowsiness, especially through eye behavior, as it is a major indicator of fatigue. The system uses advanced algorithms to detect and track the driver’s face and derive vital facial features, particularly focusing on the eyes. A trained machine learning model, developed using a large dataset of drowsiness-related facial data, is utilized in this system. This model employs both deep learning and conventional computer vision techniques to classify the driver’s responsiveness level with high accuracy. To prevent false positives, the system is carefully calibrated to differentiate between normal eye activities and drowsiness symptoms. Upon detecting fatigue, the system triggers a warning so the driver can take a rest. This safe and intelligent solution aims to reduce accidents caused by driver fatigue. It is designed to enhance driving behavior and contribute to the development of intelligent transportation systems. By using machine learning and computer vision, the system improves vehicle technology, thereby enhancing overall road safety and reducing the risk of accidents related to drowsiness.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jyoti
AU  - Nishant Kumar
AU  - Kunal
PY  - 2025
DA  - 2025/06/22
TI  - Real-Time Driver Drowsiness Detection with Machine Learning
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 756
EP  - 766
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_60
DO  - 10.2991/978-94-6463-738-0_60
ID  - 2025
ER  -