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This study refines a Kalman filter-based roll angle estimator for motorcycles, improving its accuracy in challenging scenarios and enhancing noise modeling for safer autonomous two-wheeler systems.

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Area of Science:

  • Robotics and Control Systems
  • Vehicle Dynamics and Stability

Background:

  • Growing interest in autonomous driving and active safety systems for vehicles.
  • Two-wheelers present unique challenges due to higher rider vulnerability and complex single-track vehicle (STV) dynamics.
  • Accurate estimation of roll angle is critical for STV behavior and control system design.

Purpose of the Study:

  • To refine a previously developed Kalman filter-based roll angle estimator for STVs.
  • To test the refined estimator in more challenging situations using a motorcycle multibody model.
  • To extend the method for improved noise modeling within the Kalman filter.

Main Methods:

  • Utilized a Kalman filter approach for roll angle estimation.
  • Employed a multibody model of a motorcycle for testing.
  • Incorporated an extension to enhance the modeling of noise within the filter.

Main Results:

  • The refined roll angle estimation method was successfully tested in challenging scenarios.
  • The proposed extension improved the modeling of noise within the Kalman filter.
  • The study validates enhanced estimation for motorcycle active safety systems.

Conclusions:

  • The refined Kalman filter estimator offers improved performance for motorcycle roll angle estimation.
  • Enhanced noise modeling contributes to more robust active safety system development for two-wheelers.
  • This work advances the potential for safer autonomous and semi-autonomous motorcycle technologies.