DOPIDET: Driver Drowsiness Detection


EOI: 10.11242/viva-tech.01.05.018

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Citation

Harsh Jadeja, Ahmedabbas Rakan, Riyad Chowdhury, Akshaya Prabhu, "DOPIDET: Driver Drowsiness Detection", VIVA-IJRI Volume 1, Issue 6, Article 18, pp. 1-5, 2023. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

The main idea of this research is to develop a nonintrusive system which can detect fatigue of any human and can issue a timely warning. Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy, a state which they often fail to recognize early enough. According to the expert’s studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. This system will monitor the driver's eyes using a camera and by developing an algorithm we can detect symptoms of driver fatigue early enough to avoid the person from sleeping. So, this Research will be helpful in detecting driver fatigue in advance and will give warning output in the form of alarm and popups. Moreover, the warning will be deactivated manually rather than automatically. For this purpose, a de-activation dialog will be generated which will contain some simple mathematical operation which when answered correctly will dismiss the warning. Moreover, if the driver feels drowsy there is a possibility of incorrect response to the dialog. This can judge this by plotting a graph in the time domain. If all the three input variables show a possibility of fatigue at one moment, then a Warning signal is given in the form of text and sound. This will directly give an indication of drowsiness/fatigue which can be further used as a record of driver performance.

Keywords

References

  1. K.Satish, A.Lalitesh, K. Bhargavi, M.Sishir Prem and Anjali.T “Driver Drowsiness Detection”, July 2020
  2. A Bano, A Saxena and G K Das “A Comparative Analysis of using Various Machine learning Techniques based on Drowsy Driver Detection”, Dec 2021
  3. Dhairyav Shah, Sidharth Nair, Mithil Shetty “System to Detect Driver Drowsiness”, April 2020
  4. WANGHUA DENG AND RUOXUE WU “Real-Time Driver-Drowsiness Detection System Using Facial Features”, Sep 2021
  5. Ashish Kumar and Rusha Patra “Driver Drowsiness Monitoring System using Visual Behavior and Machine Learning”, Jun 2020
  6. Shirisha vadlamudi, Ali Ahmadina, “An embedded Intelligence engine for driver drowsiness detection”, Sep 2021
  7. Challa Yashwanth and Jyoti Singh Kirar “Driver’s Drowsiness Detection”, Mar 2020
  8. Mashfiq Shahriar Zaman, Ibna Kowsar “An Algorithmic Approach to Driver Drowsiness Detection for Ensuring Safety in an Autonomous Car”, June 2020.
  9. Suhandi Junaedi and Habibullah Akbar “Driver Drowsiness Detection Based on Face”, Sep 2021.
  10. B. Mohana, C. M. Sheela Rani “Drowsiness Detection Based on Eye Closure and Yawning Detection”,Nov 2019
  11. Oraan Khunpisuth, Taweechai Chotchinasri, Varakorn Koschakosai “Driver Drowsiness Detection using Eye-Closeness Detection”, June 2021
  12. Yuexin Tian (&) , Changyuan Wang1, and Hongbo Jia “Eyes and Mouth States Detection for Drowsiness Determination”, Dec 2021
  13. Feng You, Yunbo Gong, Haiqing Tu, Jianzhong Liang, and Haiwei Wang “A Fatigue Driving Detection Algorithm Based on Facial Motion”, June2020
  14. Lei Zhao, Zengcai Wang, Xiaojin Wang, Qing Liu “Driver drowsiness detection using facial dynamic fusion information”, July 2021