Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

Masino, Johannes

doi:10.5445/KSP/1000104538

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https://library.oapen.org/bitstream/20.500.12657/48822/1/road-condition-estimation-with-data-mining-methods-using-vehicle-based-sensors.pdf
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https://library.oapen.org/bitstream/20.500.12657/48822/1/road-condition-estimation-with-data-mining-methods-using-vehicle-based-sensors.pdf
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https://library.oapen.org/bitstream/20.500.12657/48822/1/road-condition-estimation-with-data-mining-methods-using-vehicle-based-sensors.pdf
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https://library.oapen.org/bitstream/20.500.12657/48822/1/road-condition-estimation-with-data-mining-methods-using-vehicle-based-sensors.pdf

Author(s)

Masino, Johannes

Collection

AG Universitätsverlage

Language

English

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Abstract

The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation.

URI

https://directory.doabooks.org/handle/20.500.12854/70074

Keywords

Maschinelles Lernen; Fahrzeugtechnik; Straßenschäden; Fahrzeugsensorik; Maschine Learning; Vehicle Technology; Road Condition; Vehicle Sensors; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials