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A model identification scheme for driver-following dynamics in road traffic
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. 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
2013 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 6, 807-817 p.Article in journal (Refereed) Published
Abstract [en]

The driver-following, or car-following, model is one of the most fundamental driver behavior models that are applied in intelligent transport applications. Its fidelity determines the applicability of microscopic traffic simulators, where the model is often implemented to mimic real traffic. Meanwhile, the behavioral model is fundamental to the development of advanced driving assistance systems (ADAS). This paper develops a dynamic model identification approach based on iterative usage of the extended Kalman Filtering (EKF) algorithm. Among other things, this allows to carry out model identification using a rather general optimization objective on the whole physical states of the following vehicle. In particular, the method is established on the basis of the equivalence between the Kalman filter and the recursive least squares (RLS) method in a specific context of parameter identification. To illustrate the method, two car-following models are studied in numerical experiments using real car-following data. The method has shown advantages in replication and prediction of vehicle dynamics in car-following over the conventional approaches. It has also the potential to be further extended for building tactical driving controllers in intelligent transportation applications.

Place, publisher, year, edition, pages
2013. Vol. 21, no 6, 807-817 p.
Keyword [en]
Driver-following behavior, Model identification and learning, Extended Kalman filter, Recursive least square method, Traffic simulation, Advanced driving assistance systems
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-124033DOI: 10.1016/j.conengprac.2013.02.007ISI: 000318327900005Scopus ID: 2-s2.0-84875955877OAI: oai:DiVA.org:kth-124033DiVA: diva2:633944
Note

QC 20130628

Available from: 2013-06-28 Created: 2013-06-25 Last updated: 2017-12-06Bibliographically approved

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Jansson, Magnus

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf