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Optimization of system parameters for an online driving style recognition

Dorr, Dominik 1; Pandl, Konstantin D. ORCID iD icon 1; Gauterin, Frank ORCID iD icon
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

An online driving style recognition system using fuzzy logic has recently proven to work well and showed potential to optimize its parameters. This paper is about the efficient parameter optimization of such a system. To overcome combinatorial explosion, we introduce heuristics to express the main influential parameters of the system, which itself is divided into two layers. First, we use a method called Design of Experiments in order to identify the most important parameters of general high-level system parameters. The low-level layer consists of fuzzy logic systems, which are the core of the driving style recognition system. For this, we introduce a way to efficiently describe the main characteristics of a fuzzy system by very few parameters. Both sets of identified parameters are then separately optimized with an established multidimensional evolutionary algorithm. We show that using Design of Experiments is superior to a random selection of the highlevel parameters, as it increases the optimization gain by 76.5% in average. All in all, the target function, which represents a weighted classification error, was reduced by 43.9% on the test data set. ... mehr

DOI: 10.1109/ITSC.2016.7795571
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2016
Sprache Englisch
Identifikator ISBN: 978-1-5090-1889-5
KITopen-ID: 1000065759
Erschienen in IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 1–4 November 2016
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 302–307
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