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IMU-Based Road Condition Monitoring

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Christian Claudel's presentation from the 2017 CTR Symposium
http://ctr.utexas.edu/ctr-symp/

Published in: Technology
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IMU-Based Road Condition Monitoring

  1. 1. IMU-based road condition monitoring Christian Claudel Assistant Professor, CAEE University of Texas, Austin
  2. 2. Motivations  Maintaining a transportation network is one of the mandates of a transportation agency  Monitoring is usually done with expensive equipment (profilers)  Tradeoff between accuracy and refresh rate
  3. 3. Inertial measurement Units  Proposed solution: using Inertial Measurement Units (IMUs) onboard vehicles for monitoring road condition  IMUs are or will be part of future CVs (BSM requirement)  Could enable all CVs to generate road condition data
  4. 4. Challenges 1. IMUs generate vector measurements (acceleration, rotation vector, magnetic field), relative to coordinates of the device. We need self-calibrating IMUs. 2. Road condition indices, for example the Present Serviceability Rating (PSR) are usually obtained by humans. How can we reliably estimate the road condition indices from vertical acceleration timeseries data?
  5. 5. System components (Vehicular system)
  6. 6.  To determine the vertical acceleration component, we need to compute the orientation of the IMU within the vehicle  Achieved by computing a rotation matrix mapping the coordinates of the device to the coordinates of the vehicle. This computation is done automatically, after a few minutes driving. Automatic Calibration of IMUs (Mousa, et al. ACM/IEEE IPSN‘16) csR
  7. 7. In device frame In vehicle frame  Automatic calibration allows IMUs to be quickly and easily retrofitted to existing vehicles for CV applications  Applications include road condition monitoring, but also traffic monitoring
  8. 8. Road condition monitoring principle • We intend to use Supervised Learning to automatically learn the road condition grading process used by humans (when determining the PSR) Quantities derived from acceleration data Road condition metrics (PSR)
  9. 9. Timeline • Identify possible regressor variables: - Average acceleration power spectral density in different frequency bands - Number of high acceleration events (potholes) per unit distance - Peak to peak acceleration level - Vehicle speed • Collect measurement data on a subset of the road network for which PSRs are available • Train ML framework to estimate PSRs from regressors
  10. 10. Research challenges • Are regressors relevant to explain the variations of PSR data? How should the IMU be configured, and are additional measurements needed (ex: microphone)? Is the signal to noise ratio of conventional IMUs sufficient? • How fast does the ML framework converge/how much data is needed for training? • Is the ML computed framework transposable to different vehicles (within the same category)?
  11. 11. Preliminary data (UT)
  12. 12. Preliminary data (SwRI) Smooth Road
  13. 13. Preliminary data (SwRI) Rough Road
  14. 14. Vision • The road condition monitoring system could be part of a larger monitoring system, including traffic monitoring • Could be integrated with Bluetooth/WiFi readers, or Roadside Equipments (RSEs). The readers would receive measurement data from almost all vehicles vs

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