Abstract
Abstract. Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.
Highlights
Road roughness is generally considered to be the deviation of the road surface from a designed surface grade that influences safety conditions for road users (De Farias and De Souza, 2009)
The selection of a 30m width ensured the inclusion of the road surface in the data; a 5m elevation removed the impact of vertical objects along the route corridor; while a 10m length was selected on the basis of the computational cost analysis (Kumar et al, 2015a)
The developed approach is based on utilising the LiDAR intensity and elevation attributes to detect roughness regions along the road surface
Summary
Road roughness is generally considered to be the deviation of the road surface from a designed surface grade that influences safety conditions for road users (De Farias and De Souza, 2009). Several studies have indicated that the accident rate increases with increasing unevenness of the road surface (Ihs, 2004; Davies et al, 2005). They may affect rolling resistance, ride quality, vehicle operating costs and fuel consumption (Sayers and Karamihas, 1998). These roughness conditions are required to be precisely recorded, located, measured and classified in order to schedule maintenance, repair and effective management of road networks (Kumar et al, 2016). Road safety considerations must result in a road environment that should be selfexplaining and forgiving, in the sense that users are not faced with unexpected situations and their mistakes can be, if not avoided, corrected (ERSO, 2006)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
