Vehicle agile maneuvering: From rally drivers to a finite state machine approach

Manuel Acosta, Stratis Kanarachos, Mike Blundell

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    14 Citations (Scopus)
    840 Downloads (Pure)


    Rally drivers can perform extreme maneuvers and keep a vehicle on track by maximizing the vehicle agility. It is remarkable that this is achieved robustly, without a vehicle or tire model in mind. In this study, the Moment Method Diagram and Beta Method representations are used to show the maximum achievable yaw moment generated by the front and rear tires. A new maneuverability map is proposed to bypass the limitations imposed by the steady-state assumptions, based on the wheel slip - yaw moment representation. Furthermore, a simple driving automation strategy is developed to determine the sequence of inputs required for maximizing vehicle agility and negotiating extreme maneuvers. A finite state machine is designed and implemented using a two track vehicle model. The numerical results show that the finite state machine can resemble a rally driver.

    Original languageEnglish
    Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)978-1-5090-4240-1
    ISBN (Print)978-1-5090-4241-8
    Publication statusPublished - 9 Feb 2017
    Event2016 IEEE Symposium Series on Computational Intelligence - Athens, Greece
    Duration: 6 Dec 20169 Dec 2016


    Conference2016 IEEE Symposium Series on Computational Intelligence
    Abbreviated titleSSCI 2016


    • agile maneuvering
    • autonomous driving
    • drift
    • finite state machine

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems and Management
    • Control and Optimization
    • Artificial Intelligence


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