IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i7p3037-d343696.html
   My bibliography  Save this article

Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT

Author

Listed:
  • Seok-Woo Jang

    (Department of Software, Anyang University, Anyang 14028, Korea)

  • Byeongtae Ahn

    (Liberal and Arts College, Anyang University, Anyang 14028, Korea)

Abstract

In recent years, the casualties of traffic accidents caused by driving cars have been gradually increasing. In particular, there are more serious injuries and deaths than minor injuries, and the damage due to major accidents is increasing. In particular, heavy cargo trucks and high-speed bus accidents that occur during driving in the middle of the night have emerged as serious social problems. Therefore, in this study, a drowsiness prevention system was developed to prevent large-scale disasters caused by traffic accidents. In this study, machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. Additionally, a CO 2 sensor chip was used to detect additional drowsiness. Speech recognition technology can also be used to apply Speech to Text (STT), allowing a driver to request their desired music or make a call to avoid drowsiness while driving.

Suggested Citation

  • Seok-Woo Jang & Byeongtae Ahn, 2020. "Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT," Sustainability, MDPI, vol. 12(7), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:3037-:d:343696
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/7/3037/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/7/3037/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Azmat & Sebastian Kummer, 2020. "Potential applications of unmanned ground and aerial vehicles to mitigate challenges of transport and logistics-related critical success factors in the humanitarian supply chain," Asian Journal of Sustainability and Social Responsibility, Springer, vol. 5(1), pages 1-22, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Genta Miyama & Masakatsu Fukumoto & Ritsuko Kamegaya & Masahito Hitosugi, 2020. "Risk Factors for Collisions and Near-Miss Incidents Caused by Drowsy Bus Drivers," IJERPH, MDPI, vol. 17(12), pages 1-11, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammadreza Akbari & John L. Hopkins, 2022. "Digital technologies as enablers of supply chain sustainability in an emerging economy," Operations Management Research, Springer, vol. 15(3), pages 689-710, December.
    2. Yuanyuan Wu & Feng Zhu, 2021. "Junction Management for Connected and Automated Vehicles: Intersection or Roundabout?," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    3. Muhammad Irfan & Usman Habib & Fazal Muhammad & Farman Ali & Abdullah S Alwadie & Shakir Ullah & Adam Glowacz & Witold Glowacz, 2020. "Optical-Interference Mitigation in Visible Light Communication for Intelligent Transport Systems Applications," Energies, MDPI, vol. 13(19), pages 1-15, September.
    4. Shin-Ki Hong & Sung Gu Lee & Myungchin Kim, 2020. "Assessment and Mitigation of Electric Vehicle Charging Demand Impact to Transformer Aging for an Apartment Complex," Energies, MDPI, vol. 13(10), pages 1-23, May.
    5. Evelyne Tina Kassai & Muhammad Azmat & Sebastian Kummer, 2020. "Scope of Using Autonomous Trucks and Lorries for Parcel Deliveries in Urban Settings," Logistics, MDPI, vol. 4(3), pages 1-25, August.
    6. Baodi Zhang & Sheng Guo & Xin Zhang & Qicheng Xue & Lan Teng, 2020. "Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 13(8), pages 1-25, April.
    7. Shahid Hussain & Mohamed A. Ahmed & Ki-Beom Lee & Young-Chon Kim, 2020. "Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot," Energies, MDPI, vol. 13(12), pages 1-27, June.
    8. Aditya Kamat & Saket Shanker & Akhilesh Barve & Kamalakanta Muduli & Sachin Kumar Mangla & Sunil Luthra, 2022. "Uncovering interrelationships between barriers to unmanned aerial vehicles in humanitarian logistics," Operations Management Research, Springer, vol. 15(3), pages 1134-1160, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:3037-:d:343696. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.