Skip to main content

Advertisement

SpringerLink
Log in

Accident prevention and safety assistance using IOT and machine learning

  • Original Article
  • Published:
Journal of Reliable Intelligent Environments Aims and scope Submit manuscript

Abstract

Transportation is playing a vital role in our daily life and its development has made many of our chores much easier. But in recent years, driver drowsiness, distractions, and speed limit crossing cause ruinous road accidents which lead to fatalities. Slumbering, dozing, alcohol consumption cause intrusiveness which needs to alert the driver before a mishap happens. In this paper, a prototype is designed using Raspberry Pi, Pi Camera, sensors for monitoring driver’s eye movements, detecting yawning, detecting toxic gases, and alcohol consumption to prevent accidents and provide safety assistance to drivers. Internet of Things and machine learning-enabled system is implemented in vehicles for transmitting the behavior of the driver and his driving pattern to the cloud to take quick response under emergency situations. Several lives are saved by alerting the driver with help of a sound system that is deemed to prevent any distractions before happen. The cloud services and machine learning are employed in identifying fatigue drivers through the collected and stored dataset from cloud services. The device is experimentally tested, and the results show its efficiency and effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29

Similar content being viewed by others

References

  1. https://www.autocarpro.in/news-national/road-accidents-in-india-claimed-415-lives-injured-1-286-people-each-day-in-2018-44694

  2. Lee BG et al (2015) Wristband-type driver vigilance monitoring system using smartwatch. IEEE Sens J 15(10):5624–5633

    Article  Google Scholar 

  3. https://towardsdatascience.com/the-intuition-behind-facial-detection-the-viola-jones-Algorithm-29d9106b6999

  4. Hossain MY (2018) IoT based real-time drowsy driving detection system for the prevention of road accidents. ICIIBMS. https://doi.org/10.1109/iciibms.2018.8550026

    Article  Google Scholar 

  5. Dada EG et al (2017) Alcohol detection of drunk drivers with automatic car engine locking system. Nova J Eng Appl Sci 6(1):1–15

    MathSciNet  Google Scholar 

  6. Gopinath V (2017) Avoiding drunken driving road accidents by using alcoholic sensors. In: IJEAS, vol 4, Issue 5

  7. Teyeb I et al (2016) Towards a smart car seat design for drowsiness detection based on pressure distribution of the driver’s body. In: ICSEA 2016: the eleventh international conference on software engineering advances

  8. Reyes-Muñoz Angelica et al (2016) Integration of body sensor networks and vehicular ad-hoc networks for traffic safety. Sensors 16:107

    Article  Google Scholar 

  9. Archa A et al (2019) IoT based driver drowsiness detection and traffic collision avoidance system using raspberry pi. In: International journal of engineering sciences & research technology NACETEC’ 19 ISSN: 2277-9655

  10. Sharma S (2018) IoT based car accident detection and notification algorithm for general road accidents. In: IJECE, vol 9(5)

  11. Vural E et al (2007) Machine learning systems for detecting driver drowsiness. In: Proceedings, digital signal processing for in-vehicle and mobile systems, Istanbul, Turkey

  12. Jacobé C et al (2019) Detection and prediction of driver drowsiness using artificial neural network models. Accid Anal Prev 126:95–104

    Article  Google Scholar 

  13. Gupta H (2019) An IoT based air pollution monitoring system. In: ICSETS, vol 7

  14. Gromera M et al (2019) ECG sensor for detection of driver’s drowsiness. In: 23rd International conference on knowledge-based and intelligent information & engineering systems, Elsevier B.V. https://doi.org/10.1016/j.procs.2019.09.366

  15. Anitha C et al (2016) A two fold expert system for yawning detection. In: 2nd International conference on intelligent computing, communication & convergence (ICCC-2016), Elsevier. https://doi.org/10.1016/j.procs.2016.07.324

  16. Alvi U et al (2020) A comprehensive study on IoT based accident detection systems for smart vehicles, vol 8, IEEE Access. https://doi.org/10.1109/access.2020.3006887

  17. Bhatti F et al (2019) A novel internet of things-enabled accident detection and reporting system for smart city environments. Sensors 19:2071. https://doi.org/10.3390/s19092071

    Article  Google Scholar 

  18. Dashora Chirag et al (2019) IoT based framework for the detection of vehicle accident, Cluster Computing. Springer Nature, Berlin

    Google Scholar 

  19. Nanda S et al (2018) An IOT based smart system for accident prevention and detection. In: IEEE

  20. Kumar KN et al (2019) Automatic accident rescue system using IoT. In: Springer Nature 2019, International conference on computer networks and communication technologies. https://doi.org/10.1007/978-981-10-8681-6_52

  21. Zantalis F et al (2019) A review of machine learning and IoT in smart transportation. Future Internet 11:94. https://doi.org/10.3390/fi11040094

    Article  Google Scholar 

  22. Coronato A, Paragliola G (2017) A structured approach for the designing of safe AAL applications. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2017.04.058

    Article  Google Scholar 

  23. Yea J et al (2011) Situation identification techniques in pervasive computing: a review. Pervas Mob Comput. https://doi.org/10.1016/j.pmcj.2011.01.004

    Article  Google Scholar 

  24. Coronato A et al (2014) A situation-aware system for the detection of motion disorders of patients with Autism Spectrum Disorders. Expert Syst Appl Elsevier. https://doi.org/10.1016/j.eswa.2014.05.011

  25. Duttagupta S et al (2016) Performance prediction of IoT application: an experimental analysis. In: IoT’16: Proceedings of the 6th international conference on the internet of things, pp 43–51. https://doi.org/10.1145/2991561.2991572

  26. Banda G et al (2016) One IoT: an IoT protocol and framework for OEMs to make IoT-enabled devices forward compatible. J Reliable Intell Environ 2:131–144. https://doi.org/10.1007/s40860-016-0027-5

    Article  Google Scholar 

  27. Kumar SAP et al (2018) A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance. J Reliab Intell Environ 4:199–209

    Article  Google Scholar 

  28. Bacciu D et al (2017) On the need of machine learning as a service for the internet of things. In: IML ‘17: proceedings of the 1st international conference on internet of things and machine learning, vol 22, pp 1–8. https://doi.org/10.1145/3109761.3109783

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology, Tiruchirapalli, Tamil Nadu, 620015, India

    S. Uma & R. Eswari

Authors
  1. S. Uma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Eswari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Uma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Uma, S., Eswari, R. Accident prevention and safety assistance using IOT and machine learning. J Reliable Intell Environ 8, 79–103 (2022). https://doi.org/10.1007/s40860-021-00136-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40860-021-00136-3

Keywords

Search

Navigation