A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior

Fatjon Seraj, Kui Zhang, Okan Türkes, Nirvana Meratnia, Paul J.M. Havinga

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    15 Citations (Scopus)
    119 Downloads (Pure)

    Abstract

    This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.
    Original languageEnglish
    Title of host publicationUbiComp & ISWC'15
    Subtitle of host publicationproceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery (ACM)
    Pages1169-1177
    Number of pages9
    ISBN (Print)978-1-4503-3575-1
    DOIs
    Publication statusPublished - Sep 2015
    Event ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2015 ACM International Symposium on Wearable Computers - Osaka, Japan
    Duration: 7 Sep 201511 Sep 2015

    Conference

    Conference ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2015 ACM International Symposium on Wearable Computers
    Abbreviated titleUbiComp & ISWC
    CountryJapan
    CityOsaka
    Period7/09/1511/09/15

    Keywords

    • EWI-26651
    • Smartphone Sensing
    • Driver Behaviour
    • METIS-315138
    • IR-98920
    • Anomaly Detection
    • Road Monitoring

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  • Cite this

    Seraj, F., Zhang, K., Türkes, O., Meratnia, N., & Havinga, P. J. M. (2015). A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. In UbiComp & ISWC'15: proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers (pp. 1169-1177). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2800835.2800981