Model estimation for car-following dynamics based on adaptive filtering approach
2007 (English)In: 2007 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE, NEW YORK: IEEE , 2007, 684-689 p.Conference paper (Refereed)
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.
Adaptive filters, Intelligent systems, Intelligent vehicles, Iterative methods, Least squares approximation, Mathematical model, Recursive estimation, Resonance light scattering, Traffic control, Vehicle dynamics
IdentifiersURN: urn:nbn:se:kth:diva-34927DOI: 10.1109/ITSC.2007.4357741ISI: 000253972100117ScopusID: 2-s2.0-49249088182ISBN: 978-1-4244-1395-9OAI: oai:DiVA.org:kth-34927DiVA: diva2:428793
10th International IEEE Conference on Intelligent Transportation Systems Bellevue, WA, SEP 30-OCT 03, 2007