A neural-fuzzy framework for modeling car-following behavior
2006 (English)In: 2006 IEEE International Conference on Systems, Man and Cybernetics, Proceedings, IEEE , 2006, 1178-1183 p.Conference paper (Refereed)
A general framework is introduced to model driver behavior from real car-following data acquired on Swedish roads using an advanced instrumented vehicle. In early research, the data was classified into different car-following regimes based on fuzzy clustering methods and knowledge obtained from video analysis. In this paper, we propose a multi-regime framework based on the statistical property in each regime and mathematical models adopted in those regimes. This framework is an extension of TSK fuzzy inference system and can be expressed by a Neural-Fuzzy system. Genetic Algorithm (GA) is designed as the main learning method for this system. In practice, this model structure illustrates human knowledge of car-following in a more understandable manner and can be rather flexible as the regime parameters and model forms may vary according to the application context.
Place, publisher, year, edition, pages
IEEE , 2006. 1178-1183 p.
, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, ISSN 1062-922X
Automobile drivers, Behavioral research, Data acquisition, Fuzzy clustering, Image analysis, Knowledge acquisition, Neural networks
IdentifiersURN: urn:nbn:se:kth:diva-42416DOI: 10.1109/ICSMC.2006.384560ISI: 000248078501047ScopusID: 2-s2.0-34548139734ISBN: 978-1-4244-0099-7OAI: oai:DiVA.org:kth-42416DiVA: diva2:447167
2006 IEEE International Conference on Systems, Man and Cybernetics; Taipei; Taiwan; 8 October 2006 through 11 October 2006
QC 201110112011-10-112011-10-102014-11-10Bibliographically approved