An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditio...
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Autor*in:	Aymen Alshawi [verfasserIn] Stefano De Pinto [verfasserIn] Pietro Stano [verfasserIn] Sebastiaan van Aalst [verfasserIn] Kylian Praet [verfasserIn] Emilie Boulay [verfasserIn] Davide Ivone [verfasserIn] Patrick Gruber [verfasserIn] Aldo Sorniotti [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	unscented Kalman filter sideslip angle estimation vehicle speed estimation slip ratio estimation state estimation covariance matrix adaptation

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 24(2024), 2, p 436
Übergeordnetes Werk:	volume:24 ; year:2024 ; number:2, p 436

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s24020436

Katalog-ID:	DOAJ096166967

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650		4	\|a unscented Kalman filter
650		4	\|a sideslip angle estimation
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650		4	\|a slip ratio estimation
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650		4	\|a covariance matrix adaptation
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allfields	10.3390/s24020436 doi (DE-627)DOAJ096166967 (DE-599)DOAJbc15ca1951e440a093d66928f8e916e1 DE-627 ger DE-627 rakwb eng TP1-1185 Aymen Alshawi verfasserin aut An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism. unscented Kalman filter sideslip angle estimation vehicle speed estimation slip ratio estimation state estimation covariance matrix adaptation Chemical technology Stefano De Pinto verfasserin aut Pietro Stano verfasserin aut Sebastiaan van Aalst verfasserin aut Kylian Praet verfasserin aut Emilie Boulay verfasserin aut Davide Ivone verfasserin aut Patrick Gruber verfasserin aut Aldo Sorniotti verfasserin aut In Sensors MDPI AG, 2003 24(2024), 2, p 436 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:2, p 436 https://doi.org/10.3390/s24020436 kostenfrei https://doaj.org/article/bc15ca1951e440a093d66928f8e916e1 kostenfrei https://www.mdpi.com/1424-8220/24/2/436 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 2, p 436
spelling	10.3390/s24020436 doi (DE-627)DOAJ096166967 (DE-599)DOAJbc15ca1951e440a093d66928f8e916e1 DE-627 ger DE-627 rakwb eng TP1-1185 Aymen Alshawi verfasserin aut An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism. unscented Kalman filter sideslip angle estimation vehicle speed estimation slip ratio estimation state estimation covariance matrix adaptation Chemical technology Stefano De Pinto verfasserin aut Pietro Stano verfasserin aut Sebastiaan van Aalst verfasserin aut Kylian Praet verfasserin aut Emilie Boulay verfasserin aut Davide Ivone verfasserin aut Patrick Gruber verfasserin aut Aldo Sorniotti verfasserin aut In Sensors MDPI AG, 2003 24(2024), 2, p 436 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:2, p 436 https://doi.org/10.3390/s24020436 kostenfrei https://doaj.org/article/bc15ca1951e440a093d66928f8e916e1 kostenfrei https://www.mdpi.com/1424-8220/24/2/436 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 2, p 436
allfields_unstemmed	10.3390/s24020436 doi (DE-627)DOAJ096166967 (DE-599)DOAJbc15ca1951e440a093d66928f8e916e1 DE-627 ger DE-627 rakwb eng TP1-1185 Aymen Alshawi verfasserin aut An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism. unscented Kalman filter sideslip angle estimation vehicle speed estimation slip ratio estimation state estimation covariance matrix adaptation Chemical technology Stefano De Pinto verfasserin aut Pietro Stano verfasserin aut Sebastiaan van Aalst verfasserin aut Kylian Praet verfasserin aut Emilie Boulay verfasserin aut Davide Ivone verfasserin aut Patrick Gruber verfasserin aut Aldo Sorniotti verfasserin aut In Sensors MDPI AG, 2003 24(2024), 2, p 436 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:2, p 436 https://doi.org/10.3390/s24020436 kostenfrei https://doaj.org/article/bc15ca1951e440a093d66928f8e916e1 kostenfrei https://www.mdpi.com/1424-8220/24/2/436 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 2, p 436
allfieldsGer	10.3390/s24020436 doi (DE-627)DOAJ096166967 (DE-599)DOAJbc15ca1951e440a093d66928f8e916e1 DE-627 ger DE-627 rakwb eng TP1-1185 Aymen Alshawi verfasserin aut An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism. unscented Kalman filter sideslip angle estimation vehicle speed estimation slip ratio estimation state estimation covariance matrix adaptation Chemical technology Stefano De Pinto verfasserin aut Pietro Stano verfasserin aut Sebastiaan van Aalst verfasserin aut Kylian Praet verfasserin aut Emilie Boulay verfasserin aut Davide Ivone verfasserin aut Patrick Gruber verfasserin aut Aldo Sorniotti verfasserin aut In Sensors MDPI AG, 2003 24(2024), 2, p 436 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:2, p 436 https://doi.org/10.3390/s24020436 kostenfrei https://doaj.org/article/bc15ca1951e440a093d66928f8e916e1 kostenfrei https://www.mdpi.com/1424-8220/24/2/436 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 2, p 436
allfieldsSound	10.3390/s24020436 doi (DE-627)DOAJ096166967 (DE-599)DOAJbc15ca1951e440a093d66928f8e916e1 DE-627 ger DE-627 rakwb eng TP1-1185 Aymen Alshawi verfasserin aut An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism. unscented Kalman filter sideslip angle estimation vehicle speed estimation slip ratio estimation state estimation covariance matrix adaptation Chemical technology Stefano De Pinto verfasserin aut Pietro Stano verfasserin aut Sebastiaan van Aalst verfasserin aut Kylian Praet verfasserin aut Emilie Boulay verfasserin aut Davide Ivone verfasserin aut Patrick Gruber verfasserin aut Aldo Sorniotti verfasserin aut In Sensors MDPI AG, 2003 24(2024), 2, p 436 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:2, p 436 https://doi.org/10.3390/s24020436 kostenfrei https://doaj.org/article/bc15ca1951e440a093d66928f8e916e1 kostenfrei https://www.mdpi.com/1424-8220/24/2/436 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 2, p 436
language	English
source	In Sensors 24(2024), 2, p 436 volume:24 year:2024 number:2, p 436
sourceStr	In Sensors 24(2024), 2, p 436 volume:24 year:2024 number:2, p 436
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institution	findex.gbv.de
topic_facet	unscented Kalman filter sideslip angle estimation vehicle speed estimation slip ratio estimation state estimation covariance matrix adaptation Chemical technology
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callnumber-first	T - Technology
author	Aymen Alshawi
spellingShingle	Aymen Alshawi misc TP1-1185 misc unscented Kalman filter misc sideslip angle estimation misc vehicle speed estimation misc slip ratio estimation misc state estimation misc covariance matrix adaptation misc Chemical technology An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres
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topic_title	TP1-1185 An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres unscented Kalman filter sideslip angle estimation vehicle speed estimation slip ratio estimation state estimation covariance matrix adaptation
topic	misc TP1-1185 misc unscented Kalman filter misc sideslip angle estimation misc vehicle speed estimation misc slip ratio estimation misc state estimation misc covariance matrix adaptation misc Chemical technology
topic_unstemmed	misc TP1-1185 misc unscented Kalman filter misc sideslip angle estimation misc vehicle speed estimation misc slip ratio estimation misc state estimation misc covariance matrix adaptation misc Chemical technology
topic_browse	misc TP1-1185 misc unscented Kalman filter misc sideslip angle estimation misc vehicle speed estimation misc slip ratio estimation misc state estimation misc covariance matrix adaptation misc Chemical technology
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title	An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres
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title_full	An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres
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title_sort	adaptive unscented kalman filter for the estimation of the vehicle velocity components, slip angles, and slip ratios in extreme driving manoeuvres
callnumber	TP1-1185
title_auth	An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres
abstract	This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.
abstractGer	This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.
abstract_unstemmed	This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.
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container_issue	2, p 436
title_short	An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres
url	https://doi.org/10.3390/s24020436 https://doaj.org/article/bc15ca1951e440a093d66928f8e916e1 https://www.mdpi.com/1424-8220/24/2/436 https://doaj.org/toc/1424-8220
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up_date	2024-07-03T18:38:32.374Z
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An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

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