@article{ref1,
title="Analytical redundant virtual sensor design for hyper-safe driving platform",
journal="Transactions of the Korean Society of Automotive Engineers",
year="2023",
author="Park, Beomhyuk and Choi, Seungyong and Kim, Dohyun and Cho, Wanki and You, Seung-Han",
volume="31",
number="10",
pages="815-825",
abstract="In order to advance the commercialization of autonomous vehicles, it is imperative to prepare fallback for sensor and actuator failures. This study focuses on the design of robust virtual sensors to counteract failures in key sensors associated with vehicle motion, namely the yaw rate, lateral acceleration, and steering angle sensors. Analytical redundant models were developed and utilized to create alternative virtual sensors. Each virtual sensor incorporates four analytical redundant models: the left/right speed difference of the front wheel, the left/right speed difference of the rear wheel, the lateral physical model, and the induced signal from other sensors. The determination of the final alternative virtual signal is achieved through weighting the redundant models, employing a combination of vehicle domain knowledge and artificial intelligence techniques. The efficacy of the proposed virtual sensors was validated through extensive testing across various driving scenarios using the CARSIM software.<p /> <p>Language: en</p>",
language="en",
issn="1225-6382",
doi="10.7467/KSAE.2023.31.10.815",
url="http://dx.doi.org/10.7467/KSAE.2023.31.10.815"
}