Road Surface Wetness Quantification Using a Capacitive Sensor System
Road surface wetness is a contributing factor in traffic accidents. As the amount of friction reduction correlates with the water film height covering the road surface, a quantification is of high relevance in order to improve traffic safety. Both drivers and autonomous vehicles would benefit from a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9583946/ |
| _version_ | 1818580444812148736 |
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| author | Jakob Doring Andreas Beering Julia Scholtyssek Karl-Ludwig Krieger |
| author_facet | Jakob Doring Andreas Beering Julia Scholtyssek Karl-Ludwig Krieger |
| author_sort | Jakob Doring |
| collection | DOAJ |
| description | Road surface wetness is a contributing factor in traffic accidents. As the amount of friction reduction correlates with the water film height covering the road surface, a quantification is of high relevance in order to improve traffic safety. Both drivers and autonomous vehicles would benefit from additional information. This paper presents a novel concept for road wetness quantification. It is based on a <inline-formula> <tex-math notation="LaTeX">$2\times 4$ </tex-math></inline-formula>-planar capacitive transducer array, capable to detect water spray ejected by the tires and its wetness-related dependencies. The reliable assessment of these dependencies by a proposed capacitive sensor system is shown in an experimental study on an asphalt circuit for various wheel speeds. Besides the spray’s correlation with speed, the results reveal significant differences in transducer positions and designs confirming the array’s relevance regarding wetness quantification. In addition, a 1-nearest neighbor classifier capable of automatically distinguishing between eight wetness levels is proposed. The classifier is optimized by an extended version of balanced accuracy and reaches similar performance as binary classifiers from related research. A balanced ratio between capacitance increase-, standard deviation- and speed-related feature types is one key aspect of classifier performance. Furthermore, up to a certain extent, the array’s individual transducers can significantly contribute to classifier performance with design- and position-related advantages. |
| first_indexed | 2024-12-16T07:17:42Z |
| format | Article |
| id | doaj.art-b464d8dd27914f4093519c8d5fcf57d9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T07:17:42Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b464d8dd27914f4093519c8d5fcf57d92022-12-21T22:39:45ZengIEEEIEEE Access2169-35362021-01-01914549814551210.1109/ACCESS.2021.31210999583946Road Surface Wetness Quantification Using a Capacitive Sensor SystemJakob Doring0https://orcid.org/0000-0002-1418-749XAndreas Beering1https://orcid.org/0000-0002-8098-2926Julia Scholtyssek2https://orcid.org/0000-0002-5335-9389Karl-Ludwig Krieger3Institute of Electrodynamics and Microelectronics, University of Bremen, Bremen, GermanyInstitute of Electrodynamics and Microelectronics, University of Bremen, Bremen, GermanyInstitute of Electrodynamics and Microelectronics, University of Bremen, Bremen, GermanyInstitute of Electrodynamics and Microelectronics, University of Bremen, Bremen, GermanyRoad surface wetness is a contributing factor in traffic accidents. As the amount of friction reduction correlates with the water film height covering the road surface, a quantification is of high relevance in order to improve traffic safety. Both drivers and autonomous vehicles would benefit from additional information. This paper presents a novel concept for road wetness quantification. It is based on a <inline-formula> <tex-math notation="LaTeX">$2\times 4$ </tex-math></inline-formula>-planar capacitive transducer array, capable to detect water spray ejected by the tires and its wetness-related dependencies. The reliable assessment of these dependencies by a proposed capacitive sensor system is shown in an experimental study on an asphalt circuit for various wheel speeds. Besides the spray’s correlation with speed, the results reveal significant differences in transducer positions and designs confirming the array’s relevance regarding wetness quantification. In addition, a 1-nearest neighbor classifier capable of automatically distinguishing between eight wetness levels is proposed. The classifier is optimized by an extended version of balanced accuracy and reaches similar performance as binary classifiers from related research. A balanced ratio between capacitance increase-, standard deviation- and speed-related feature types is one key aspect of classifier performance. Furthermore, up to a certain extent, the array’s individual transducers can significantly contribute to classifier performance with design- and position-related advantages.https://ieeexplore.ieee.org/document/9583946/Capacitive sensorsdriver assistanceroad surface wetness detectionvehicle safetywetness classification |
| spellingShingle | Jakob Doring Andreas Beering Julia Scholtyssek Karl-Ludwig Krieger Road Surface Wetness Quantification Using a Capacitive Sensor System IEEE Access Capacitive sensors driver assistance road surface wetness detection vehicle safety wetness classification |
| title | Road Surface Wetness Quantification Using a Capacitive Sensor System |
| title_full | Road Surface Wetness Quantification Using a Capacitive Sensor System |
| title_fullStr | Road Surface Wetness Quantification Using a Capacitive Sensor System |
| title_full_unstemmed | Road Surface Wetness Quantification Using a Capacitive Sensor System |
| title_short | Road Surface Wetness Quantification Using a Capacitive Sensor System |
| title_sort | road surface wetness quantification using a capacitive sensor system |
| topic | Capacitive sensors driver assistance road surface wetness detection vehicle safety wetness classification |
| url | https://ieeexplore.ieee.org/document/9583946/ |
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