Skip to main content

A Proposed Solution to Road Traffic Accidents Based on Fuzzy Logic Control

  • Conference paper
  • First Online:
Digital Technologies and Applications (ICDTA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 211))

Included in the following conference series:

  • 2580 Accesses

Abstract

Road Traffic accidents are one of the main problems causing death and fatal injuries in all countries. Several general suggestions were presented to the ministry of transport to try to overcome this problem. This paper discusses a specific approach based on fuzzy logic control to train semi-autonomous cars to prevent accidents from occurring. These types of cars equipped with many devices along with decision making algorithms based on learning will make cars more intelligent to take the right decision when no reaction of the driver is noticed.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. NHTSA | National Highway Traffic Safety Administration. https://www.nhtsa.gov/. Accessed 27 Jun 2020

  2. Fagnant DJ, Kockelman K (2015) Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp Res Part A: Policy Pract 77:167–181

    Google Scholar 

  3. Shinde T (2013) Car anti-collision and intercommunication system using communication protocol. Int J Sci Res (IJSR) 2(6):187–191

    MathSciNet  Google Scholar 

  4. Coelingh E, Eidehall A, Bengtsson M (2010) Collision warning with full auto brake and pedestrian detection - a practical example of automatic emergency braking. In: IEEE conference on intelligent transportation systems, proceedings, ITSC, pp 155–160

    Google Scholar 

  5. Drissi Touzani H, Faquir S, Yahyaouy A (2020) Data mining techniques to analyze traffic accidents data: case application in Morocco. In: The fourth international conference on intelligent computing in data sciences ICDS 2020 conference, pp 1–3

    Google Scholar 

  6. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-III. Inf Sci 9(1):43–80

    Article  MathSciNet  Google Scholar 

  7. Driss M, Saint-Gerand T, BenSaïd A, Benabdeli K, Hamadouche MA (2013) A fuzzy logic model for identifying spatial degrees of exposure to the risk of road accidents (Case study of the Wilaya of Mascara, Northwest of Algeria). In: 2013 international conference on advanced logistics and transport, ICALT 2013, pp 69–74

    Google Scholar 

  8. Bates JHT, Young MP (2003) Applying fuzzy logic to medical decision making in the intensive care unit. Am J Respir Crit Care Med 167(7):948–952

    Article  Google Scholar 

  9. Zbinden AM, Feigenwinter P, Petersen-Felix S, Hacisalihzade S (1995) Arterial pressure control with isoflurane using fuzzy logic. Br J Anaesth 74(1):66–72

    Article  Google Scholar 

  10. Faquir S, Yahyaouy A, Tairi H, Sabor J (2016) Energy management in a hybrid PV/wind/battery system using a type-1 fuzzy logic computer algorithm. Int J Intell Eng Inform 4(3–4):229–244

    Google Scholar 

  11. El Amrani R, Faquir S, Yahyaouy A, Tairi H (2018) Modelling and implementation of an energy management simulator based on agents using optimised fuzzy rules: Application to an electric vehicle. Int J Innovative Comput Appl 9(4):203–215

    Article  Google Scholar 

  12. Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller—part I. IEEE Trans Syst Man Cybern 20(2):404–418

    Article  Google Scholar 

  13. Guru99. Fuzzy Logic Tutorial: What is, Application & Example. https://www.guru99.com/what-is-fuzzy-logic.html. Accessed 08 Oct 2020

  14. Roychowdhury S, Pedrycz W (2001) A survey of defuzzification strategies. Int J Intell Syst 16(6):679–695

    Article  Google Scholar 

  15. Ondruš J, Kolla E, Vertaľ P, Šarić Ž (2020) How do autonomous cars work? Transp Res Procedia 44(2019):226–233

    Article  Google Scholar 

  16. Sarkan B, Stopka O, Gnap J, Caban J (2017) Investigation of exhaust emissions of vehicles with the spark ignition engine within emission control. Procedia Eng 187:775–782

    Article  Google Scholar 

  17. Yun HS, Kim TH, Park TH (2019) Speed-bump detection for autonomous vehicles by lidar and camera. J Electr Eng Technol 14(5):2155–2162

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Touzani, H.D., Faquir, S., Senhaji, S., Yahyaouy, A. (2021). A Proposed Solution to Road Traffic Accidents Based on Fuzzy Logic Control. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_6

Download citation

Publish with us

Policies and ethics