Teaching a Vehicle to Autonomously Drift: A Data-based Approach Using Neural Networks

Manuel Acosta, Stratis Kanarachos

    Research output: Contribution to journalArticlepeer-review

    26 Citations (Scopus)
    318 Downloads (Pure)


    This paper presents a novel approach to teach a vehicle how to drift, in a
    similar manner that professional drivers do. Specically, a hybrid structure
    formed by a Model Predictive Controller and feedforward Neural Networks
    is employed for this purpose. The novelty of this work lies in a) the adoption
    of a data-based approach to achieve autonomous drifting along a wide range
    of road radii and body slip angles, and b) in the implementation of a road
    terrain classier to adjust the system actuation depending on the current
    friction characteristics. The presented drift control system is implemented
    in a multi-actuated ground vehicle equipped with active front steering and
    in-wheel electric motors and trained to drift by a real test driver using a
    driver-in-the-loop setup. Its performance is veried in the simulation environment
    IPG-CarMaker through dierent open loop and path following
    drifting manoeuvres.
    Original languageEnglish
    Pages (from-to)12-28
    Number of pages17
    JournalKnowledge-Based Systems
    Early online date11 Apr 2018
    Publication statusPublished - 1 Aug 2018

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems, [153], (2018) DOI: 10.1016/j.knosys.2018.04.015


    • Neural Networks
    • Autonomous Drift Control
    • Autonomous Vehicles
    • Multi-Actuated Ground Vehicles
    • Model Predictive Control


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