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

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, a...

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Bibliographic Details
Published in:Journal of advanced transportation 2018-01, Vol.2018 (2018), p.1-10
Main Authors: Reischl, Markus, Gauterin, Frank, Frey, Michael, Levasseur, Guillaume, Thumm, Jakob, Masino, Johannes, Mikut, Ralf
Format: Article
Language:English
Subjects:
Asphalt
Barriers
Cameras
Classification
Classifiers
Data mining
Datasets
Feature extraction
Graphical user interface
Gyroscopes
Highway construction
Microelectromechanical systems
Railroad crossings
Road conditions
Roads
Roads & highways
Sensors
Support vector machines
Traffic accidents & safety
Transportation
Vehicles
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Summary: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.
ISSN:0197-6729
2042-3195
DOI:10.1155/2018/8647607