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 proceedingpeer-review

    31 Citations (Scopus)


    —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
    Number of pages6
    ISBN (Electronic)9781538669594
    Publication statusPublished - 24 May 2019
    EventIEEE 2019 International Conference on Mechatronics - Technische Universität Ilmenau, Ilmenau, Germany
    Duration: 19 Mar 201921 Mar 2019


    ConferenceIEEE 2019 International Conference on Mechatronics
    Internet address


    • 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


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