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Model estimation for car-following dynamics based on adaptive filtering approach
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-6855-5868
2007 (English)In: 2007 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE, NEW YORK: IEEE , 2007, 684-689 p.Conference paper (Refereed)
Abstract [en]

Identification of driver behavior models using data has been an essential problem for the development of high-fidelity micro-simulation and design of vehicle-based intelligent systems. In this research, our focus is on model estimation of car-following, a crucial element of tactical driver behavior, using data collected from real traffic. By theoretical exploration of the relation between the Kalman filter and the recursive least square (RLS) method, a mathematical model estimation framework is proposed based on iterative usage of the extended Kalman filter (EKF). Numerical experiments have been conducted in the estimation and evaluation of a generalized GM model using closed-loop simulations. Accordingly, the applicability of the approach has been identified with further research potential.

Place, publisher, year, edition, pages
NEW YORK: IEEE , 2007. 684-689 p.
Keyword [en]
Adaptive filters, Intelligent systems, Intelligent vehicles, Iterative methods, Least squares approximation, Mathematical model, Recursive estimation, Resonance light scattering, Traffic control, Vehicle dynamics
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-34927DOI: 10.1109/ITSC.2007.4357741ISI: 000253972100117ScopusID: 2-s2.0-49249088182ISBN: 978-1-4244-1395-9OAI: diva2:428793
10th International IEEE Conference on Intelligent Transportation Systems Bellevue, WA, SEP 30-OCT 03, 2007
Available from: 2011-07-01 Created: 2011-06-17 Last updated: 2012-02-12Bibliographically approved

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ReferencesLink to record
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