Scipediacontent: Scipediacontent moved page Draft Content 872868627 to Levasseur et al 2018a

2021-02-03T14:36:45Z

Scipediacontent moved page Draft Content 872868627 to Levasseur et al 2018a

Scipediacontent: 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..."

2021-02-03T14:36:42Z

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..."

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== 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 ==
<pdf>Media:Draft_Content_872868627-beopen4982-5836-document.pdf</pdf>

== Original document ==

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

* [http://downloads.hindawi.com/journals/jat/2018/8647607.pdf http://downloads.hindawi.com/journals/jat/2018/8647607.pdf] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.1155/2018/8647607 http://dx.doi.org/10.1155/2018/8647607] under the license cc-by

* [http://dx.doi.org/10.5445/ir/1000087378 http://dx.doi.org/10.5445/ir/1000087378] under the license http://creativecommons.org/licenses/by/4.0

* [http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/283442 http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/283442] under the license https://creativecommons.org/licenses/by/4.0/deed.de

* [http://downloads.hindawi.com/journals/jat/2018/8647607.pdf http://downloads.hindawi.com/journals/jat/2018/8647607.pdf],
: [http://downloads.hindawi.com/journals/jat/2018/8647607.xml http://downloads.hindawi.com/journals/jat/2018/8647607.xml],
: [http://dx.doi.org/10.1155/2018/8647607 http://dx.doi.org/10.1155/2018/8647607]

* [http://dx.doi.org/10.1155/2018/8647607 http://dx.doi.org/10.1155/2018/8647607],
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license http://creativecommons.org/licenses/by/4.0/

* [https://publikationen.bibliothek.kit.edu/1000087378 https://publikationen.bibliothek.kit.edu/1000087378],
: [https://publikationen.bibliothek.kit.edu/1000087378/19311497 https://publikationen.bibliothek.kit.edu/1000087378/19311497],
: [https://doi.org/10.5445/IR/1000087378 https://doi.org/10.5445/IR/1000087378],
: [http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:swb:90-873784 http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:swb:90-873784]

* [https://www.hindawi.com/journals/jat/2018/8647607 https://www.hindawi.com/journals/jat/2018/8647607],
: [http://downloads.hindawi.com/journals/jat/2018/8647607.pdf 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://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],
: [https://publikationen.bibliothek.kit.edu/1000087378/19311497 https://publikationen.bibliothek.kit.edu/1000087378/19311497],
: [https://academic.microsoft.com/#/detail/2896845551 https://academic.microsoft.com/#/detail/2896845551]

* [ ]

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

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Levasseur et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 872868627 to Levasseur et al 2018a

Scipediacontent: 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..."