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Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters

Masino, Johannes 1; Thumm, Jakob 1; Levasseur, Guillaume; Frey, Michael ORCID iD icon 1; Gauterin, Frank ORCID iD icon 1; Mikut, Ralf ORCID iD icon 2; Reischl, Markus ORCID iD icon 2
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)
2 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)


This work aims at classifying the road condition with data mining methods using simple acceleration sensors and gyroscopes installed in vehicles. Two classifiers are developed with a support vector machine (SVM) to distinguish between different types of road surfaces, such as asphalt and concrete, and obstacles, such as potholes or railway crossings. From the sensor signals, frequency-based features are extracted, evaluated automatically with MANOVA. The selected features and their meaning to predict the classes are discussed. The best features are used for designing the classifiers. Finally, the methods, which are developed and applied in this work, are implemented in a Matlab toolbox with a graphical user interface. The toolbox visualizes the classification results on maps, thus enabling manual verification of the results. The accuracy of the cross-validation of classifying obstacles yields 81.0% on average and of classifying road material 96.1% on average. The results are discussed on a comprehensive exemplary data set.

Verlagsausgabe §
DOI: 10.5445/IR/1000087378
Veröffentlicht am 12.11.2018
DOI: 10.1155/2018/8647607
Zitationen: 7
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 10.2018
Sprache Englisch
Identifikator ISSN: 0197-6729, 2042-3195
KITopen-ID: 1000087378
HGF-Programm 47.01.02 (POF III, LK 01) Biol.Netzwerke u.Synth.Regulat. IAI
Erschienen in Journal of advanced transportation
Verlag Hindawi
Band 2018
Seiten Article: 8647607
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 22.10.2018
Nachgewiesen in Scopus
Web of Science
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