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Using ensemble of decision trees with SVM nodes to learn the behaviour of a transmission control software

: Guan, Tianyi; Frey, Christian W.

Postprint urn:nbn:de:0011-n-3236916 (493 KByte PDF)
MD5 Fingerprint: cd7b75c13a80bdb16757fc227ef6a49d
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Erstellt am: 27.1.2015

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 17th International Conference on Intelligent Transportation Systems, ITSC 2014. Vol.2 : Qingdao, China, October 8-11, 2014
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-6078-1
ISBN: 978-1-4799-6079-8
International Conference on Intelligent Transportation Systems (ITSC) <17, 2014, Qingdao/China>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Energy efficiency has become a major issue in trade, transportation and environment protection. While the next generation of zero emission propulsion systems are still under development, it is already possible to increase fuel efficiency in regular vehicles by applying a more fuel efficient driving behaviour. The ensemble classifier presented in this paper is part of an adaptive manufacturer independent fuel efficiency assistant that only uses publicly available FMS-2 CAN-Bus data. The goal is to learn the basic behaviour of an unknown automatic transmission control software, only by investigating available input and output data. The knowledge can then be used to e.g. predict the fuel consumption of a vehicle or be used for other purposes that are not subject to this specific paper. The classifier consists of random ensembles of global and local classification trees, whose nodes are binary Support Vector Machines. To the best knowledge of the authors, it is the first time this specific kind of classifier has been formulated and used to learn the behaviour of an unknown software based on rudimentary input and output data.