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Driving to safety: real-time danger spot and drowsiness monitoring system

Abstract

Development of safety features to prevent accident on the roads is one of the major challenges in the automobile industry. Driving with no prior information about the upcoming road can be dangerous. Not knowing about what’s coming down the road can disbalance the vehicle and lead to accident. Driving when tired or drunk can lead to major life risking accidents. The road accidents can be prevented by collecting the data of road’s characteristics and providing an alert to the driver if any distraction comes in the way. This paper introduces a concept of real-time monitoring of the driver and generating an alert when the driver gets sleepy or unconscious. Road analysis was also done to classify spots with high possibility of accidents and alerts generated for the same. The system thus covers two major reasons which cause heavy accidents on the road and provides solution to overcome them.

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References

  1. Assari MA, & Rahmati M (2011) Driver drowsiness detection using face expression recognition. In: 2011 IEEE international conference on signal and image processing applications (ICSIPA), pp. 337–341

  2. Astarita V, Vittoria-Caruso M, Danieli G, Carmine-Festa D, Pasquale GV, Iuele T, Vaiana R (2012) A mobile application for road surface quality control: UNIquALRoad. Soc Behav Sci 54:1135–1144

    Article  Google Scholar 

  3. Bham GH, Leu MC, Vallati M, Mathur DR (2014) Driving simulator validation of driver behavior with limited safe vantage points for data collection in work zones. J Safety Res 49:53-e1

    Article  Google Scholar 

  4. Bharadwaj S, Murthy S, Varaprasad G (2013) Detection of potholes in autonomous vehicle. IET Intel Transp Syst 8(6):543–549

    Article  Google Scholar 

  5. CDC. (2018) WISQARS (Web-based Injury Statistics Query and Reporting System). (2018) Atlanta, GA: US Department of Health and Human Services, CDC; 2018. Available at https://www.cdc.gov/injury/wisqars. Accessed January 4

  6. Chen K, Lu M, Fan, X, Wei M, and Wu J (2011) Road Condition monitoring using on-board three-axis accelerometer and GPS sensor. In: proceedings of international ICST conference on communication and networking, pp.1032–1037

  7. Chong M, Abraham A, & Paprzycki M (2005) Traffic accident analysis using machine learning paradigms. Informatica, 29(1)

  8. Eriksson J, Girod L, Hull B, Newton R, Madden S, & Balakrishnan H (2008) The pothole patrol: using a mobile sensor network for road surface monitoring. In: proceedings of the 6th international conference on mobile systems, applications, and services, pp. 29–39

  9. Fazeen M, Gozick B, Dantu R, Bhukhiya M, González MC (2012) Safe driving using mobile phones. IEEE Trans Intell Transp Syst 13(3):1462–1468

    Article  Google Scholar 

  10. Ghadge M, Pandey D, & Kalbande D (2015). Machine learning approach for predicting bumps on road. In: Proceedings 2015 international conference on applied and theoretical computing and communication technology (ICATCCT), pp. 481–485

  11. Horne JA, Reyner LA (1995) Sleep related vehicle accidents. BMJ 310(6979):565–567

    Article  Google Scholar 

  12. Jap BT, Lal S, Fischer P, Bekiaris E (2009) Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst Appl 36(2):2352–2359

    Article  Google Scholar 

  13. Jothi S, Priyanka S, Yuvaraj P, & Kalaivani S (2016) Automatic detection of potholes and humps on roads to aid drivers. Int J Adv Res Manag Archit Technol Eng 2(3)

  14. Khan MI, & Mansoor AB (2008) Real time eyes tracking and classification for driver fatigue detection. In: proceedings international conference image analysis and recognition, pp. 729–738, Springer, Berlin

  15. Lin J, & Liu Y (2010) Potholes detection based on SVM in the pavement distress image. In: proceedings 2010 ninth international symposium on distributed computing and applications to business engineering and science (DCABES), pp. 544–547

  16. Madli R, Hebbar S, Pattar P, Golla V (2015) Automatic detection and notification of potholes and humps on roads to aid drivers. IEEE Sens J 15(8):4313–4318

    Article  Google Scholar 

  17. Mednis A, Strazdins G, Zviedris R, Kanonirs G, & Selavo L (2011). Real time pothole detection using android smartphones with accelerometers. In: proceedings international conference on distributed computing in sensor systems and workshops (DCOSS), pp. 1–6

  18. Mohan P, Padmanabhan VN, & Ramjee R (2008) Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: proceedings 6th ACM conference on embedded network sensor systems, pp. 323–336

  19. Mortazavi A, Eskandarian A, Sayed RA (2009) Effect of drowsiness on driving performance variables of commercial vehicle drivers. Int J Automot Technol 10(3):391–404

    Article  Google Scholar 

  20. National Highway Traffic Safety Administration (NHTSA), (2016) https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812456

  21. Prapulla SB, Rao SN, & Herur VA (2017) Road quality analysis and mapping for faster and safer travel. In: proceedings international conference on energy, communication, data analytics and soft computing (ICECDS), pp. 2487–2490

  22. Tesema TB, Abraham A, Grosan C (2005) Rule mining and classification of road traffic accidents using adaptive regression trees. Int J Simul 6(10–11):80–94

    Google Scholar 

  23. Vural E, Çetin M, Erçil A, Littlewort G, Bartlett M, & Movellan J (2009) Machine learning systems for detecting driver drowsiness. In: proceedings in-vehicle corpus and signal processing for driver behavior, pp. 97–110. Springer, Boston, MA

  24. Wards Intelligence (2016), http://subscribers.wardsintelligence.com/analysis/world-vehicle-population-rose-46-2016

  25. World Health Organization (WHO). Global Status Report on Road Safety 2018. December 2018. [cited 2019 April 8]. Available from URL: https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/external icon

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Correspondence to Hoang Pham.

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Kumar, V., Pham, H., Pandey, P.K. et al. Driving to safety: real-time danger spot and drowsiness monitoring system. Soft Comput 25, 14479–14497 (2021). https://doi.org/10.1007/s00500-021-06381-1

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Keywords

  • Drowsiness detection
  • Eye aspect ratio
  • GPS—global positioning system
  • Potholes
  • Harsh braking region
  • Sharp turns
  • Alert system
  • Driving aid