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DOI: 10.5445/IR/1000087378
Veröffentlicht am 12.11.2018

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

Masino, Johannes; Thumm, Jakob; Levasseur, Guillaume; Frey, Michael; Gauterin, Frank; Mikut, Ralf; Reischl, Markus

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.

Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 0197-6729, 2042-3195
URN: urn:nbn:de:swb:90-873784
KITopen-ID: 1000087378
HGF-Programm 47.01.02 (POF III, LK 01)
Erschienen in Journal of advanced transportation
Band 2018
Seiten Article: 8647607
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
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