Machine learning algorithms for wet road surface detection using acoustic measurements

Stratis Kanarachos, Mike Blundell, Olivier Haas, Michalis Kalliris, Rigas Kotsakis

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

—Precipitation can adversely influence road safety.
Slippery road conditions have traditionally been detected using
reactive methods requiring considerable excitation of the tire
forces. Alternatives rely on non-contact methods such as vision,
sound or ultrasonic sensors. This study proposes a cost-effective
wet road conditions detection method based on acoustic
measurements for urban and highway driving. It compared the
performance of a range of machine learning algorithms to
classify the road condition based on the audio features
calculated using octave-band frequency analysis. The approach
was evaluated experimentally using data collected from a
vehicle instrumented with a microphone, GPS and CAN bus
data logger. Support Vector Machines using Quadratic and
Cubic kernels, as well as Logistic Regression performed better
compared to other machine learning-based methods
Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Mechatronics, ICM 2019
PublisherIEEE
Pages265-270
Number of pages6
Volume(In-press)
ISBN (Electronic)9781538669594
DOIs
Publication statusPublished - 24 May 2019
EventIEEE 2019 International Conference on Mechatronics - Technische Universität Ilmenau, Ilmenau, Germany
Duration: 19 Mar 201921 Mar 2019
https://ieee-icm2019.org

Conference

ConferenceIEEE 2019 International Conference on Mechatronics
CountryGermany
CityIlmenau
Period19/03/1921/03/19
Internet address

Fingerprint

Learning algorithms
Learning systems
Acoustics
Ultrasonic sensors
Microphones
Frequency bands
Support vector machines
Global positioning system
Logistics
Acoustic waves
Costs

Keywords

  • acoustic measurements
  • wet road surface detection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Automotive Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Industrial and Manufacturing Engineering

Cite this

Kanarachos, S., Blundell, M., Haas, O., Kalliris, M., & Kotsakis, R. (2019). Machine learning algorithms for wet road surface detection using acoustic measurements. In Proceedings - 2019 IEEE International Conference on Mechatronics, ICM 2019 (Vol. (In-press), pp. 265-270). [8722834] IEEE. https://doi.org/10.1109/ICMECH.2019.8722834

Machine learning algorithms for wet road surface detection using acoustic measurements. / Kanarachos, Stratis; Blundell, Mike; Haas, Olivier; Kalliris, Michalis; Kotsakis, Rigas.

Proceedings - 2019 IEEE International Conference on Mechatronics, ICM 2019. Vol. (In-press) IEEE, 2019. p. 265-270 8722834.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Kanarachos, S, Blundell, M, Haas, O, Kalliris, M & Kotsakis, R 2019, Machine learning algorithms for wet road surface detection using acoustic measurements. in Proceedings - 2019 IEEE International Conference on Mechatronics, ICM 2019. vol. (In-press), 8722834, IEEE, pp. 265-270, IEEE 2019 International Conference on Mechatronics, Ilmenau, Germany, 19/03/19. https://doi.org/10.1109/ICMECH.2019.8722834
Kanarachos S, Blundell M, Haas O, Kalliris M, Kotsakis R. Machine learning algorithms for wet road surface detection using acoustic measurements. In Proceedings - 2019 IEEE International Conference on Mechatronics, ICM 2019. Vol. (In-press). IEEE. 2019. p. 265-270. 8722834 https://doi.org/10.1109/ICMECH.2019.8722834
Kanarachos, Stratis ; Blundell, Mike ; Haas, Olivier ; Kalliris, Michalis ; Kotsakis, Rigas. / Machine learning algorithms for wet road surface detection using acoustic measurements. Proceedings - 2019 IEEE International Conference on Mechatronics, ICM 2019. Vol. (In-press) IEEE, 2019. pp. 265-270
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