Abstract
The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity.
Highlights
There are no published standards for using Light Detection and Ranging (LiDAR) to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity
A Pearson test was performed for both cases and p-values for each analysis of 0.000 suggest there is statistical significance to reject the null hypothesis of no linear correlation
This study explored the use of intensity detect pavement markings are and will continue to be important for transportation agendata evaluate pavementmanufacturers
Summary
The United States has over 8.8 million lane miles nationwide, which all require regular maintenance and evaluation of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Pavement markings deteriorate overtime due to tire wear, weather, and snowplow wear, making the markings difficult-to-detect for human and autonomous vehicles. Determining locations where vehicles cannot detect pavement markings is especially important in the new frontier of connected and autonomous vehicles. Found that approximately 30% of state agencies conduct annual pavement marking evaluations, and the remaining 70% do so on a bi-annual or more sporadic basis [1]. A limitation of this method is that the unit only detects retroreflectivity on one pavement marking at a time. This paper evaluates using LiDAR sensors to provide scalable methods that will allow agencies to systematically evaluate their road markings and routinely program their maintenance activities
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