Acta Scientific Computer Sciences

Review Article Volume 4 Issue 11

Automatic Drowsy Driver Recognizer System Using Machine Learning Model

Damodharan D*, Anandhan K and Anupam Lakhanpal

Assistant Professor, School of computing science and Technology, Galgotias University, Greater Noida, India

*Corresponding Author: Damodharan D, Assistant Professor, School of computing science and Technology, Galgotias University, Greater Noida, India.

Received: July 25, 2022; Published: October 19, 2022

Reprints View PDF Related Articles

Abstract

Drivers who do not take periodic rest while long driving process a more chances of becoming drowsy, a condition in which they sometimes are unable to identify soon enough the danger. Research found that nearly twenty five percent (25%) of all severe motor way mishap occur due to inactive drivers in need of a break, which means that drowsiness, is responsible for high number of accidents as compared to drink driving. Alert help can notify of sleepiness in situation of risky speed and alert driver about their present condition of tiredness, provide convertible reactivity and if a warning come forth, signaling the local useful points in the portal.

Keywords: Alert; Drowsiness; Drowsy; Portal; Sleepiness

References

  1. Driver fatigue and road accidents a literature review and position paper, Royal Society for the prevention of Accidents, FEB (2021).
  2. R Jabbar., et al. “Driver drowsiness detection model using convolutional neural networks techniques for android application”. in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), IEEE, (2020).
  3. D Mollicone., et al. “Predicting performance and safety based on driver fatigue”. Accident Analysis Prevention 126 (2019): 142-145.
  4. D Singh., et al. “Security issues in iot and their countermeasures in smart city applications”. Advanced Computing and Intelligent Engineering (2020): 301-313.
  5. M Miranda., et al. “Portable prevention and monitoring of driver's drowsiness focuses to eyelid movement using internet of things”. in 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM) (2018): 1-5.
  6. S Abraham., et al. “Enhancing vehicle safety with drowsiness detection and collision avoidance”. International Journal of Pure and Applied Mathematics (2018): 2295-2310.
  7. K Saleh., et al. “Driving behavior classification based on sensor data fusion using lstm recurrent neural networks”. in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (2017): 1-7.
  8. Y Ed-Doughmi., et al. “Real-time system for driver fatigue detection based on a recurrent neuronal network”. Journal of Imaging3 (2020): 8.
  9. M Babaeian., et al. “Real-time driver drowsiness detection using wavelet transform and ensemble logistic regression”. International Journal of Intelligent Transportation Systems Research3 (2019): 212-222.
  10. A A Hayawi and J Waleed. “Driver's drowsiness monitoring and alarming auto-system based on EOG signals”. in 2019 2nd International Conference on Engineering Technology and its Applications (IICETA) (2019): 214-218.
  11. Y Ma., et al. “Driving fatigue detection from EEG using a modified PCANet method”. Computational Intelligence and Neuro-science (2019): 1-9.
  12. R V Siva Reddy and P A Kumari. “Internet of things (IoT) based multilevel drunken driving detection and prevention system using Raspberry Pi 3”. International Journal of Computer Science and Information Security (IJCSIS) (2020): 131-137.
  13. S W Jang and B Ahn. “Implementation of detection system for drowsy driving prevention using image recognition and iot”. Sustainability7 (2020): 3037.
  14. D Singh and B K Pattanayak. “Markovian model analysis for energy harvesting nodes in a modified opportunistic routing protocol”. International Journal of Electronics12 (2020): 1963-1984.
  15. S S Kulkarni., et al. “Image processing for driver's safety and vehicle control using raspberry Pi and webcam”. in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) (2017): 1288-1291.
  16. D Samanta., et al. “Wireless sensor networks model for monitoring system based on iot”. Solid State Technology (2020).

Citation

Citation: Damodharan D., et al. “Automatic Drowsy Driver Recognizer System Using Machine Learning Model". Acta Scientific Computer Sciences 4.11 (2022): 25-30.

Copyright

Copyright: © 2022 Damodharan D., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

Indexed In




News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is December 25, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"

Contact US





+91 9182824667

     info@acta-scientific.com

Creative Commons License Open Access by Acta Scientific is licensed under a Creative Commons Attribution 4.0 International License
Based on a work at https://actascientific.com

ff

Acta-Publications

ff

© 2021 Acta Scientific, All rights reserved.