IDEAS home Printed from https://ideas.repec.org/h/spr/prochp/978-3-031-56576-2_13.html
   My bibliography  Save this book chapter

Machine Learning-Based Forward Collision Avoidance System: A Case Study for the Kayoola EVS

In: Artificial Intelligence Tools and Applications in Embedded and Mobile Systems

Author

Listed:
  • Ali Ziryawulawo

    (The Nelson Mandela African Institution of Science and Technology)

  • Adonia Mbarebaki

    (Kiira Motors Corporation)

  • Sam Anael

    (The Nelson Mandela African Institution of Science and Technology)

Abstract

A forward collision avoidance system is an advanced driver assistance system that alerts the driver or maneuvers for safe motion in case of the occurrence of an imminent collision. In this research, an efficient reinforcement learning algorithm that actuates the car to move forward, steer left, right, and stop was designed for autonomous vehicles. Currently, forward collision avoidance systems are based on input commands from the sensors like Lidar and Camera to the system and the output is based on the commands initialized. With this model, the vehicle gathers data using an RGB Camera and collision sensor while moving on the road in a simulated environment. Scenarios are developed which include cars moving around corners, straight road, and in a more urban layout with other obstacles like cars within the environment. Reward flags are given for no collision and penalty for collision with obstacles within the environment. Model testing was done in Carla’s simulator and analysis of the model was done on a Tensor board and recorded simulation as the vehicle moves within the environment. An optimized deep Q-learning algorithm that relies on deep reinforcement learning was developed under constrained conditions in a Carla simulation environment with an overall accuracy of 64% and a metric loss of 20%. The algorithm relies on dynamic programming which can buffer data during the training process.

Suggested Citation

  • Ali Ziryawulawo & Adonia Mbarebaki & Sam Anael, 2024. "Machine Learning-Based Forward Collision Avoidance System: A Case Study for the Kayoola EVS," Progress in IS, in: Jorge Marx Gómez & Anael Elikana Sam & Devotha Godfrey Nyambo (ed.), Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, pages 139-153, Springer.
  • Handle: RePEc:spr:prochp:978-3-031-56576-2_13
    DOI: 10.1007/978-3-031-56576-2_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:prochp:978-3-031-56576-2_13. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.