Optimized mobile retroreflectivity unit data processing algorithms : [project summary].
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2017-06-01
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Abstract:Researchers examined both hardware and
software components of the MRU to determine
where improvements could be made. The MRU’s laser makes one-meter sweeps, which
detect retroreflective striping and measure its reflectivity. The MRU also detects other
reflective markings such as raised pavement markers, stop bars, etc. Using characteristics of
the reflectance signals acquired by the MRU, the researchers developed a part of the FRS to
distinguish striping signals from other signals. Improvements were made to the MRU power
supply when it was found that the power supply was contributing to data inconsistency.
Similarly, other hardware components of the MRU which were found to influence the accuracy
and repeatability of measurements were corrected.
With the ability to distinguish between striping and other roadway markings, the researchers
examined striping data and methods of interpreting them. The reflectance signal generally
mimics the profile of the striping, but the researchers found that in addition to variations in
the reflectance signal due to the striping itself, there was also an effect based on how well the
striping was centered in the one-meter laser path. Also, lane striping tended to be more worn
on its median side, giving the reflectance signal a lopsided appearance. The FRS corrected for
these effects and improved the accuracy and repeatability of basic MRU data collection. The
FRS interface was also designed to facilitate operator learning and use, making control of MRU
settings and data interpretation more straightforward.
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