Have a personal or library account? Click to login
Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness Cover

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Open Access
|May 2023

References

  1. P. Inthanon and S. Mungsing, “Detection of Drowsiness from Facial Images in Real-Time Video Media using Nvidia Jetson Nano,” 2020 17th International Conf. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2020, pp. 246–249, doi: 10.1109/ECTI-CON49241.2020.9158235.
  2. A. Dasgupta, D. Rahman and A. Routray, “A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 11, 2019, pp. 4045–4054, doi: 10.1109/TITS.2018.2879609.
  3. M. Ramzan et al., “A Survey on State-of-the-Art Drowsiness Detection Techniques,” IEEE Access, vol. 7, 2019 pp. 61904–61919, doi: 10.1109/ACCESS.2019.2914373.
  4. K.G. Seifert, T. Jan and T. Karnahl, “Don’t Sleep and Drive – VW’s Fatigue Detection Technology,” Proc. 19th International Technical Conf. Enhanced Safety of Vehicles (ESV), 2005.
  5. R.J. Sternberg, Cognitive Psychology, Cengage Learning Press, 2012.
  6. I. Biederman and P. Kalocsai, “Neural and Psychophysical Analysis of Object and Face Recognition.” Face Recognition, Springer, 1998, pp. 3–25.
  7. A. Ellis and R.M. Grieger, Handbook of Rational-Emotive Therapy, Vol. 2, Springer, 1986.
  8. J. Qiang and X. Yang, “Real-time eye, gaze, and face pose tracking for monitoring driver vigilance,” International Journal of Real-Time Imaging, vol. 8, no. 5, 2002, pp. 357–377, doi: 10.1006/rtim.2002.0279.
  9. D. Chauhan et al. “An effective face recognition system based on Cloud-based IoT with a deep learning model.” Microprocessors and Microsystems, vol. 81, 2021, pp. 103726.
  10. V.J. Pillai et al., “Fixed Angle Video Frame Diminution Technique for Vehicle Speed Detection,” Annals of the Romanian Society for Cell Biology, vol. 25, no. 2, 2021, pp. 3204–3210.
  11. S. Hu and G. Zheng, “Driver drowsiness detection with eyelid related parameters by Support Vector Machine,” Expert Systems with Applications, vol. 36, no. 4, 2009, pp. 7651–7658, doi: 10.1016/j.eswa.2008.09.030.
  12. B.R. Prathap and K. Ramesha. “Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context.” International Journal of Digital Crime and Forensics (IJDCF), vol. 12, no. 4, 2020, pp. 1–19, doi: 10.4018/IJDCF.2020100101.
  13. T. Hamada et al., “Detecting method for Driver’s drowsiness applicable to Individual Features,” IEEE Proc. Intelligent Transportation Systems, vol. 2, 2003, pp. 1405–1410, doi: 10.1109/ITSC.2003.1252715.
  14. L. Barr et al., “A review and evaluation of emerging driver fatigue detection, measures and technologies,” A Report of U.S. Department of Transportation, 2009.
  15. M. Eriksson and N.P. Papanikolopoulos, “Eyetracking for detection of driver fatigue,” IEEE Proc. Intelligent Transport Systems, 1999, pp. 314–318, doi: 10.1109/ITSC.1997.660494.
  16. A. Eskandarian and A. Mortazavi, “Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection,” IEEE Intelligent Vehicles Symposium (IV’07), Istanbul, Turkey, 2007, pp. 553–559, doi: 10.1109/IVS.2007.4290173.
  17. R. Grace et al., “A Drowsy Driver Detection System for Heavy Vehicles,” Digital Avionics Systems Conference, 1998. Proceedings, 17th DASC. The AIAA/IEEE/SAE, vol. 2, 1998, pp. 50–70, doi: 10.1109/DASC.1998.739878.
  18. M.T. De Mello et al., “Sleep disorders as a Cause of Motor Vehicle Collisions.” International Journal of Preventive Medicine, vol. 4, no. 3, 2003, pp. 246–257.
  19. M. Shahverdy et al., “Driver Behavior Detection and Classification Using Deep Convolutional Neural Networks,” Expert Systems with Applications, vol. 149, 2020, pp. 113240, doi.org/10.1016/j.eswa.2020.113240.
  20. V.A. Valsan, P.P. Mathai and I. Babu, “Monitoring Driver’s Drowsiness Status at Night Based on Computer Vision,” 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 989-993 (2021). doi: 10.1109/ICCCIS51004.2021.9397180.
  21. J.W. Baek et al., “Real-time Drowsiness Detection Algorithm for Driver State Monitoring Systems,” 2018 Tenth International Conf. Ubiquitous and Future Networks (ICUFN), 2018, pp. 73–75, doi: 10.1109/ICUFN.2018.8436988.
  22. Rivelli, Elizabeth. “Drowsy Driving 2021 Facts and Statistics | Bankrate.” Drowsy Driving 2021 Facts & Statistics | Bankrate, www.bankrate.com/insurance/car/drowsy-driving-statistics. Accessed 27 Jan. 2023.
DOI: https://doi.org/10.14313/jamris/2-2022/17 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649
Language: English
Page range: 64 - 73
Submitted on: Feb 9, 2022
Accepted on: May 3, 2022
Published on: May 29, 2023
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Boppuru Rudra Prathap, Kukatlapalli Pradeep Kumar, Javid Hussain, Cherukuri Ravindranath Chowdary, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.