Volume : 9, Issue : 6, JUN 2023

NATIONAL CONFERENCE ON INNOVATIONS IN COMPUTING TECHNOLOGIES (NCICT'23)

VEHICLE SECURITY BY HUMAN EYE BLINK

G.SUDHA, V. PONMANI, E.BOTHIVISHNUPRAKASH, R.RAHUL

Abstract

This project developed a prototype integrated system that combines machine vision-based drowsy driver monitoring technology and accident prevention system analysis of operator/vehicle performance to reliably assess driver drowsiness. Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. They increase the amounts of deaths and fatalities injuries globally. In this project, a module for Advanced Driver Assistance System is presented to reduce the number of accidents due to driver’s fatigue and hence increase the transportation safety. The purpose of this system will be reliably quantifying commercial motor vehicle driver drowsiness and provide a real-time warning to the driver and/or a control output to the commercial steering or other systems as warranted.

Article : Download PDF

Cite This Article

-

Article No : 11

Number of Downloads : 1370

References

1. GSudha, PPavithra, PPriya, MManikandan. Alcohol Sensor based vehicle ignition control system using Arduino UNO. The primary motivation behind this under takingis" Drunken driving detector". pp.2582-5208. irjmets.com.2020

2. National Center for Statistics and Analysis.(2018).Early Estimate of motor Vehicle Traffic Fatalities for the First Half (Jan- Jun) of 2018. Accessed: Feb. 12,2019.

3. L. Moreira-Matias and H. Farah, “On developing adriver identification methodology using in-vehicle data recorders,” IEEE Trans. In tell. Transp. Syst., vol. 18,no.9,pp.2387–2396,Sep.2017.

4. J. Kim et al., “Impact of the face angle to travelling trajectory during the riding standing-type personal mobility device,” in Proc. Web Conf. (MATEC), vol.161,Apr.2018,Art.no.003001.

5. A.Kashevniketal., “Context-based cyclist intelligent support: An approach to e-bike control based on smart phone sensors.” In Proc.Int.Conf.NextGener.Wired/WirelessNetw.Cham,Switzerland:Springer,Aug.2018,pp.16–22.

6. A.Smirnov, A.Kashevnik, I.Lashkov, N.Hashimoto, and A. Boyali, “Smartphone-based two-wheeled self-balancing vehicles rider assistant, ”inProc. 17th IEEE Conf. Open Innov. Assoc. (FRUCT), Apr.2015,pp.201–209.

7. A.Smirnov, A.Kashevnik, and I.Lashkov, “Human-smart phone interaction for dangerous situation detection and recommendation generation while driving,”in Proc.18^th Int.Conf. Speech Comput., Aug.2016,pp.346–353.

8. I. Lashkov, A. Smirnov, A. Kashevnik, and V.Parfenov, “Ontology based approach and implementation of ADAS system for mobile device use while driving,” in Proc. 6th Int. Conf. Knowl. Eng.SemanticWeb, Sep.2015,pp.117–131.

9. A.Smirnov, A.Kashevnik, I.Lashkov, O.Baraniuc, and V.Parfenov, “Smart phone-based identification of dangerous driving situations: Algorithms and implementation,” in Proc. 18th IEEEConf. Open Innov. Assoc. (FRUCT), Apr. 2016, pp.306–313.

10. A.Fedotov, I.Lashkov, and A.Kashevnik, “Web-service for drive safely system user analysis: Architecture and implementation,” inProc.22^nd Conf. Open Innov. Assoc. (FRUCT), May2018, pp.40–47.

11. S. Sivaraman and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,” IEEET rans. Intell. Transp. Syst., vol. 14, no.4, pp. 1773–1795, Dec.2013.

12. G Sudha, MBirunda, JGnanasoundharam, KGokulKannan, RPraveena, TRGaneshBabu. Distracted Driver Detection Using Deep Learning Classifier of Image Net Models.(ICAISS)2022.

13. D.Kim, J.Choi, H.Yoo, U.Yang, and K.Sohn, “Rear obstacle detection system with fisheye stereo camera using HCT,” Expert Syst. Appl., vol. 42, nos.17–18,pp. 6295–6305,Oct. 2015.

14. Z. Kim, “Robust Lane detection and tracking inchallenging scenarios,” IEEE Trans. Intell. Transp.Syst.,vol.9,no.1,pp.16–26,Mar.2008.

15. J.Jo, S.J.Lee, J.Kim, H.G.Jung, and K.R.Park, “Vision-based method for detecting driver drowsiness and distraction in driver monitoring system,” Proc.SPIE, vol.50, no.12, Dec.2011, Art.no.127202.

16. H.Li, H.Wang, L.Liu, and M.Gruteser, “Automatic unusual driving event identification for dependable self-driving,” in Proc. 16th ACM Conf.Embedded Netw. Sensor Syst., New York,NY, USA,Nov.2018,pp. 15–27.

17. C.-W.Youetal., “Car Safe app: Alerting drowsy and distracted drivers using dual cameras on smart phones,” in Proc. 11th Annu. Int. Conf. Mobile Syst., Appl.,Services,Jun.2013,pp.13–26.

18. C.-W.Youetal., “Car Safe app: Alerting drowsy and distracted drivers using dual cameras on smartphones,” in Proc. 11th Annu. Int. Conf. MobileSyst.,Appl.,Services,Jun.2013,pp.13–26.

19. G Sudha,L Bhuvaneshwari, J Deepika. Human wearable device using IoT. Volume 11, Issue 5, pp.576-:579.2020

20. S.Ferreira, Z.Kokkinogenis, and A.Couto, “Using real-life alert based data to analyse drowsiness and distraction of commercial drivers,” Transp. Res.F, Traffic Psychol. Behav., vol.60,pp.25–36,Jan.2019.