Vicente, B.A.H., James, S.S. and Anderson, S.R. (2021) Linear system identification versus physical modeling of lateral-longitudinal vehicle dynamics. IEEE Transactions on Control Systems Technology, 29 (3). pp. 1380-1387. ISSN 1063-6536
Abstract
Accurate physical modeling of vehicle dynamics requires extensive a priori knowledge of the studied vehicle. In contrast, data-driven modeling approaches require only a set of data that are a good account of the vehicle's driving envelope. In this brief, we compare, for the first time, the prediction capabilities of both approaches applied to a large-scale real-world driving data set. The data set contains several cornering maneuvers, acceleration, and deceleration stages and was collected over public roads. Linear and nonlinear physical models were identified through nonlinear optimization of their unknown parameters. Closed-form subspace identification methods were used to initialize the estimate of a linear state-space model, and the initialization was then refined through nonlinear optimization. The optimized models were validated against 59 km of independent driving data. The model fits, in the longitudinal velocity, were 68.9% versus 80.2% for the nonlinear physical model and linear data-driven (second-order) model, respectively, and, in the yaw rate, 43.0% versus 63.5%. These results show that, for this vehicle, a simple linear data-driven model outperformed both linear and nonlinear physical models under real-world driving conditions. This has important implications for control design approaches in autonomous vehicles.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Nonlinear parameter estimation; state-space modeling; subspace identification; system identification; vehicle dynamics |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 DREAM4CARS 731593 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Jul 2020 16:23 |
Last Modified: | 24 May 2022 11:55 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tcst.2020.2994120 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163604 |