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This study introduces a low-cost system using on-car sensors to detect road surface anomalies in real-time. The method effectively identifies various pavement distresses, improving road maintenance and safety.

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Area of Science:

  • Civil Engineering
  • Transportation Engineering
  • Signal Processing

Background:

  • Road infrastructure is critical for transportation, but increasing traffic and aging lead to surface degradation.
  • Timely detection of road surface anomalies is essential for maintenance and safety.

Purpose of the Study:

  • To develop a low-cost, real-time system for screening road pavement conditions using on-car sensors.
  • To differentiate between localized (potholes, manhole covers) and widespread (fatigue cracking, rutting) pavement distresses.

Main Methods:

  • Utilizing acceleration signals from on-car sensors (dashboard, floorboard).
  • Processing signals in the time-frequency domain via short-time Fourier transform.
  • Extracting features like coefficient of variation and entropy for distress classification.
  • Employing supervised machine learning classifiers for pavement distress identification.

Main Results:

  • Demonstrated effectiveness in detecting the presence and type of road surface distress.
  • Achieved high classification rates in distinguishing between different types of pavement anomalies.
  • Validated system performance using real-world, manually labeled data.

Conclusions:

  • The proposed system offers an effective and low-cost solution for real-time road condition monitoring.
  • Accurate detection and classification of pavement distresses can significantly aid in proactive road maintenance.
  • Integration of on-car sensors and machine learning provides a promising approach for intelligent transportation systems.