@article{ref1,
title="Roll angle estimation of a motorcycle through inertial measurements",
journal="Sensors (Basel)",
year="2021",
author="Maceira, Diego and Luaces, Alberto and Lugrís, Urbano and Naya, Miguel Á and Sanjurjo, Emilio",
volume="21",
number="19",
pages="e6626-e6626",
abstract="Currently, the interest in creating autonomous driving vehicles and progressively more sophisticated active safety systems is growing enormously, being a prevailing importance factor for the end user when choosing between either one or another commercial vehicle model. While four-wheelers are ahead in the adoption of these systems, the development for two-wheelers is beginning to gain importance within the sector. This makes sense, since the vulnerability for the driver is much higher in these vehicles compared to traditional four-wheelers. The particular dynamics and stability that govern the behavior of single-track vehicles (STVs) make the task of designing active control systems, such as Anti-lock Braking System (ABS) systems or active or semi-active suspension systems, particularly challenging. The roll angle can achieve high values, which greatly affects the general behavior of the vehicle. Therefore, it is a magnitude of the utmost importance; however, its accurate measurement or estimation is far from trivial. This work is based on a previous paper, in which a roll angle estimator based on the Kalman filter was presented and tested on an instrumented bicycle. In this work, a further refinement of the method is proposed, and it is tested in more challenging situations using the multibody model of a motorcycle. Moreover, an extension of the method is also presented to improve the way noise is modeled within this Kalman filter.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21196626",
url="http://dx.doi.org/10.3390/s21196626"
}