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Smart pothole detection system using vehicle-mounted sensors and machine learning

  • Ali Anaissi
  • Nguyen Lu Dang Khoa
  • Thierry Rakotoarivelo
  • Mehrisadat Makki Alamdari
  • Yang Wang
Original Paper
  • 11 Downloads

Abstract

Road networks are critical assets supporting economies and communities. Despite budget and time constraints, road authorities strive to maintain them to ensure safety, ongoing service, and economic productivity. This paper proposes a virtual road network inspector (VRNI), which continuously monitors road conditions and provides decision support to managers and engineers. VRNI uses acceleration data from vehicle-mounted sensors to assess road conditions. It proposes a novel road damage detection method based on two adaptive one-class support vector machine models, which were applied on the vertical and lateral acceleration data. We evaluated this method on data from a real deployment on school buses in New South Wales, Australia. Experimental results show that our method consistently detects 97.5% of the road damage with a 4% false alarm rate that relate to benign anomalies such as expansion joints.

Keywords

Road condition assessment Machine learning One-class support vector machine Accelerometers Sensors Pothole 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Engineering and ITThe University of SydneyCamperdownAustralia
  2. 2.Data61|CSIRO, EveleighCanberraAustralia
  3. 3.School of Civil and Environmental EngineeringUniversity of New South WalesSydneyAustralia

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