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Advanced Vehicle State Monitoring:: Evaluating Moving Horizon Estimators and Unscented Kalman Filter
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. National Engineering Laboratory for Electric Vehicles and the Collaborative Innovation Centre for Electric Vehicles in Beijing.ORCID iD: 0000-0002-4504-6059
School of Mechnical Engineering, Beijing Institute of Technology.
Department of Electrical Engineering, Chalmers University of Technology.
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Vehicle Dynamics.ORCID iD: 0000-0001-8928-0368
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2019 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 68, no 6, p. 5430-5442, article id 8682143Article in journal (Refereed) Published
Abstract [en]

Active safety systems must be used to manipulate the dynamics of autonomous vehicles to ensure safety. To this end, accurate vehicle information, such as the longitudinal and lateral velocities, is crucial. Measuring these states, however, can be expensive, and the measurements can be polluted by noise. The available solutions often resort to Bayesian filters such as the Kalman filter, but can be vulnerable and erroneous when the underlying assumptions do not hold. With its clear merits in handling nonlinearities and uncertainties, moving horizon estimation (MHE) can potentially solve the problem and is thus studied for vehicle state estimation. This paper designs an unscented Kalman filter, standard MHE, modified MHE and recursive least squares MHE to estimate critical vehicle states, respectively. All the estimators are formulated based upon a highly nonlinear vehicle model that is shown to be locally observable. The convergence rate, accuracy and robustness of the four estimation algorithms are comprehensively characterised and compared under three different driving manoeuvres. For MHE-based algorithms, the effects of horizon length and optimisation techniques on the computational efficiency and accuracy are also investigated.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 68, no 6, p. 5430-5442, article id 8682143
Keywords [en]
Tires, Computational modeling, Wheels, Kalman filters, Adaptation models, State estimation
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering; SRA - Transport
Identifiers
URN: urn:nbn:se:kth:diva-249386DOI: 10.1109/TVT.2019.2909590ISI: 000472563200020Scopus ID: 2-s2.0-85067814058OAI: oai:DiVA.org:kth-249386DiVA, id: diva2:1304274
Funder
TrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20190626

Available from: 2019-04-11 Created: 2019-04-11 Last updated: 2019-07-31Bibliographically approved

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