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Vehicle Classification using Road Side Sensors and Feature-free Data Smashing Approach
Luleå University of Technology, Luleå, Sweden.
Aalto University, Helsinki, Finland.ORCID-id: 0000-0001-8613-6176
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).ORCID-id: 0000-0003-1460-2988
Luleå University of Technology, Luleå, Sweden.ORCID-id: 0000-0002-5888-8626
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2016 (Engelska)Ingår i: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Piscataway: IEEE , 2016, s. 1988-1993, artikel-id 7795877Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The main contribution of this paper is a study of the applicability of data smashing - a recently proposed data mining method - for vehicle classification according to the "Nordic system for intelligent classification of vehicles" standard, using measurements of road surface vibrations and magnetic field disturbances caused by passing vehicles. The main advantage of the studied classification approach is that it, in contrast to the most of traditional machine learning algorithms, does not require the extraction of features from raw signals. The proposed classification approach was evaluated on a large dataset consisting of signals from 3074 vehicles. Hence, a good estimate of the actual classification rate was obtained. The performance was compared to the previously reported results on the same problem for logistic regression. Our results show the potential trade-off between classification accuracy and classification method's development efforts could be achieved.

Ort, förlag, år, upplaga, sidor
Piscataway: IEEE , 2016. s. 1988-1993, artikel-id 7795877
Nyckelord [en]
Data mining, Economic and social effects, Intelligent systems, Intelligent vehicle highway systems, Learning algorithms, Learning systems, Magnetic levitation vehicles, Roads and streets, Transportation, Vehicles, Mechanical vibrations
Nationell ämneskategori
Farkostteknik Signalbehandling Datorseende och robotik (autonoma system) Reglerteknik
Identifikatorer
URN: urn:nbn:se:hh:diva-35664DOI: 10.1109/ITSC.2016.7795877ISI: 000392215500310Scopus ID: 2-s2.0-85010042316ISBN: 978-1-5090-1889-5 (digital)ISBN: 978-1-5090-1888-8 (digital)ISBN: 978-1-5090-1890-1 (tryckt)OAI: oai:DiVA.org:hh-35664DiVA, id: diva2:1165283
Konferens
19th IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 01-04, 2016, Rio de Janeiro, Brazil
Tillgänglig från: 2017-12-13 Skapad: 2017-12-13 Senast uppdaterad: 2018-03-23Bibliografiskt granskad

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Hostettler, RolandLyamin, NikitaBirk, WolfgangWiklund, Urban

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FarkostteknikSignalbehandlingDatorseende och robotik (autonoma system)Reglerteknik

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