IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3535-d1068626.html
   My bibliography  Save this article

Vision-Based Ingenious Lane Departure Warning System for Autonomous Vehicles

Author

Listed:
  • Sudha Anbalagan

    (Centre for Smart Grid Technologies, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, India)

  • Ponnada Srividya

    (NGNLab, Department of Computer Technology, Anna University, MIT Campus, Chennai 600044, India)

  • B. Thilaksurya

    (NGNLab, Department of Computer Technology, Anna University, MIT Campus, Chennai 600044, India)

  • Sai Ganesh Senthivel

    (Information Networking Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

  • G. Suganeshwari

    (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, India)

  • Gunasekaran Raja

    (NGNLab, Department of Computer Technology, Anna University, MIT Campus, Chennai 600044, India)

Abstract

Lane detection is necessary for developing intelligent Autonomous Vehicles (AVs). Using vision-based lane detection is more cost-effective, requiring less operational power. Images captured by the moving vehicle include varying brightness, blur, and occlusion caused due to diverse locations. We propose a Vision-based Ingenious Lane Departure Warning System (VILDS) for AV to address these challenges. The Generative Adversarial Networks (GAN) of the VILDS choose the most precise features to create images that are identical to the original but have better clarity. The system also uses Long Short-Term Memory (LSTM) to learn the average behavior of the samples to forecast lanes based on a live feed of processed images, which predicts incomplete lanes and increases the reliability of the AV’s trajectory. Further, we devise a strategy to improve the Lane Departure Warning System (LDWS) by determining the angle and direction of deviation to predict the AV’s Lane crossover. An extensive evaluation of the proposed VILDS system demonstrated the effective working of the lane detection and departure warning system modules with an accuracy of 98.2% and 96.5%, respectively.

Suggested Citation

  • Sudha Anbalagan & Ponnada Srividya & B. Thilaksurya & Sai Ganesh Senthivel & G. Suganeshwari & Gunasekaran Raja, 2023. "Vision-Based Ingenious Lane Departure Warning System for Autonomous Vehicles," Sustainability, MDPI, vol. 15(4), pages 1-11, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3535-:d:1068626
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3535/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3535/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Swapnil Waykole & Nirajan Shiwakoti & Peter Stasinopoulos, 2021. "Review on Lane Detection and Tracking Algorithms of Advanced Driver Assistance System," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    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. Manoj Kumar Kalra & Sanjay Kumar Shukla & Ashutosh Trivedi, 2023. "Track-Index-Guided Sustainable Off-Road Operations Using Visual Analytics, Image Intelligence and Optimal Delineation of Track Features," Sustainability, MDPI, vol. 15(10), pages 1-16, May.

    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. Swapnil Waykole & Nirajan Shiwakoti & Peter Stasinopoulos, 2022. "Performance Evaluation of Lane Detection and Tracking Algorithm Based on Learning-Based Approach for Autonomous Vehicle," Sustainability, MDPI, vol. 14(19), pages 1-20, September.

    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:15:y:2023:i:4:p:3535-:d:1068626. 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.