Paper Contents
Abstract
In order to forecast a driver's state of mind and emotions and to deliver information that will increase road safety, machine learning techniques have been applied. It uses artificial intelligence in some way. Bio-indicators, a driver's conduct while driving, and facial expressions can all be used to gauge a driver's health. We provide an exhaustive review of recent efforts on driver sleepiness detection and alert systems in this paper. We also discuss the machine learning algorithm, HAAR-based cascade classifier, and OpenCV that are employed to assess the state of the driver. Finally, we list the difficulties the current systems and present the corresponding research opportunities. Due to their distinctive driving characteristics, humans have different driving methods, knowledge, and attitudes. Several research projects have investigated the issue of identifying abnormal driving behavior by using computer vision techniques to examine the driver's frontal face and vehicle dynamics. Yet, complex driver behavior aspects cannot be captured by standard approaches. However, since the development of deep learning architectures, a lot of research has been done on employing neural network algorithms to assess and identify driver drowsiness.
Copyright
Copyright © 2023 Subashree S. This is an open access article distributed under the Creative Commons Attribution License.
