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Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network

Vehicle dynamics control systems have a fundamental role in smart and autonomous mobility, where one of the most crucial aspects is the vehicle body velocity estimation. In this paper, the problem of a correct evaluation of the vehicle longitudinal velocity for dynamic control applications is approa...

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Autores principales: Napolitano Dell’Annunziata, Guido, Arricale, Vincenzo Maria, Farroni, Flavio, Genovese, Andrea, Pasquino, Nicola, Tranquillo, Giuseppe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739250/
https://www.ncbi.nlm.nih.gov/pubmed/36502221
http://dx.doi.org/10.3390/s22239516
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author Napolitano Dell’Annunziata, Guido
Arricale, Vincenzo Maria
Farroni, Flavio
Genovese, Andrea
Pasquino, Nicola
Tranquillo, Giuseppe
author_facet Napolitano Dell’Annunziata, Guido
Arricale, Vincenzo Maria
Farroni, Flavio
Genovese, Andrea
Pasquino, Nicola
Tranquillo, Giuseppe
author_sort Napolitano Dell’Annunziata, Guido
collection PubMed
description Vehicle dynamics control systems have a fundamental role in smart and autonomous mobility, where one of the most crucial aspects is the vehicle body velocity estimation. In this paper, the problem of a correct evaluation of the vehicle longitudinal velocity for dynamic control applications is approached using a neural networks technique employing a set of measured samples referring to signals usually available on-board, such as longitudinal and lateral acceleration, steering angle, yaw rate and linear wheel speed. Experiments were run on four professional driving circuits with very different characteristics, and the vehicle longitudinal velocity was estimated with different neural network training policies and validated through comparison with the measurements of the one acquired at the vehicle’s center of gravity, provided by an optical Correvit sensor, which serves as the reference (and, therefore, exact) velocity values. The results obtained with the proposed methodology are in good agreement with the reference values in almost all tested conditions, covering both the linear and the nonlinear behavior of the car, proving that artificial neural networks can be efficiently employed onboard, thereby enriching the standard set of control and safety-related electronics.
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spelling pubmed-97392502022-12-11 Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network Napolitano Dell’Annunziata, Guido Arricale, Vincenzo Maria Farroni, Flavio Genovese, Andrea Pasquino, Nicola Tranquillo, Giuseppe Sensors (Basel) Article Vehicle dynamics control systems have a fundamental role in smart and autonomous mobility, where one of the most crucial aspects is the vehicle body velocity estimation. In this paper, the problem of a correct evaluation of the vehicle longitudinal velocity for dynamic control applications is approached using a neural networks technique employing a set of measured samples referring to signals usually available on-board, such as longitudinal and lateral acceleration, steering angle, yaw rate and linear wheel speed. Experiments were run on four professional driving circuits with very different characteristics, and the vehicle longitudinal velocity was estimated with different neural network training policies and validated through comparison with the measurements of the one acquired at the vehicle’s center of gravity, provided by an optical Correvit sensor, which serves as the reference (and, therefore, exact) velocity values. The results obtained with the proposed methodology are in good agreement with the reference values in almost all tested conditions, covering both the linear and the nonlinear behavior of the car, proving that artificial neural networks can be efficiently employed onboard, thereby enriching the standard set of control and safety-related electronics. MDPI 2022-12-06 /pmc/articles/PMC9739250/ /pubmed/36502221 http://dx.doi.org/10.3390/s22239516 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Napolitano Dell’Annunziata, Guido
Arricale, Vincenzo Maria
Farroni, Flavio
Genovese, Andrea
Pasquino, Nicola
Tranquillo, Giuseppe
Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network
title Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network
title_full Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network
title_fullStr Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network
title_full_unstemmed Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network
title_short Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network
title_sort estimation of vehicle longitudinal velocity with artificial neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739250/
https://www.ncbi.nlm.nih.gov/pubmed/36502221
http://dx.doi.org/10.3390/s22239516
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