(Created page with " == Abstract == This work aims at classifying the road condition with data mining methods using simple acceleration sensors and gyroscopes installed in vehicles. Two classifi...")
 
m (Scipediacontent moved page Draft Content 872868627 to Levasseur et al 2018a)
 
(No difference)

Latest revision as of 15:36, 3 February 2021

Abstract

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.

SCOPUS: ar.j

info:eu-repo/semantics/published

Document type: Article

Full document

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document

Original document

The different versions of the original document can be found in:

http://downloads.hindawi.com/journals/jat/2018/8647607.xml,
http://dx.doi.org/10.1155/2018/8647607
https://doaj.org/toc/0197-6729,
https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/
https://publikationen.bibliothek.kit.edu/1000087378/19311497,
https://doi.org/10.5445/IR/1000087378,
http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:swb:90-873784
http://downloads.hindawi.com/journals/jat/2018/8647607.pdf,
https://difusion.ulb.ac.be/vufind/Record/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/283442/Holdings,
https://publikationen.bibliothek.kit.edu/1000087378,
https://publikationen.bibliothek.kit.edu/1000087378/19311497,
https://academic.microsoft.com/#/detail/2896845551
  • [ ]



DOIS: 10.1155/2018/8647607 10.5445/ir/1000087378

Back to Top

Document information

Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1155/2018/8647607
Licence: Other

Document Score

0

Views 6
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?