IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i8p4572-d539782.html
   My bibliography  Save this article

Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

Author

Listed:
  • Jiří David

    (Department of Mechanical and Electrical Engineering, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic)

  • Pavel Brom

    (Department of Quantitative Methods, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic)

  • František Starý

    (Department of Mechanical and Electrical Engineering, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic)

  • Josef Bradáč

    (Department of Mechanical and Electrical Engineering, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic)

  • Vojtěch Dynybyl

    (Department of Mechanical and Electrical Engineering, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic)

Abstract

This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

Suggested Citation

  • Jiří David & Pavel Brom & František Starý & Josef Bradáč & Vojtěch Dynybyl, 2021. "Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control," Sustainability, MDPI, vol. 13(8), pages 1-25, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4572-:d:539782
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/8/4572/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/8/4572/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maode Yan & Jiacheng Song & Lei Zuo & Panpan Yang, 2017. "Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback," Energies, MDPI, vol. 10(11), pages 1-17, November.
    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. Saeed Vasebi & Yeganeh M. Hayeri, 2021. "Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    2. Yi-Jen Mon, 2022. "Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology," Sustainability, MDPI, vol. 14(9), pages 1-14, April.

    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.

      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:13:y:2021:i:8:p:4572-:d:539782. 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.