Highway Cross-Slope Measurement using Mobile LiDAR

Abstract

Ensuring adequate pavement cross-slope on highways can improve driver safety by reducing the potential for ponding to occur or vehicles to hydroplane. Mobile laser scanning (MLS) systems provide a rapid, continuous, and cost-effective means of collecting accurate 3D coordinate data along a corridor in the form of a point cloud. This study provides an evaluation of MLS systems in terms of the accuracy and precision of collected cross-slope data and documentation of procedures needed to calibrate, collect, and process this data. Mobile light detection and ranging (LiDAR) data were collected by five different vendors on three roadway sections. The results indicate the difference between ground control adjusted and unadjusted LiDAR derived cross-slopes, and field surveying measurements less than 0.19% at a 95% confidence level. The unadjusted LiDAR data incorporated corrections from an integrated inertial measurement unit and high-accuracy real-time kinematic GPS, however it was not post-processed adjusted with ground control points. This level of accuracy meets suggested cross-slope accuracies for mobile measurements (±0.2%) and demonstrates that mobile LiDAR is a reliable method for cross-slope verification. Performing cross-slope verification can ensure existing pavement meets minimum cross-slope requirements, and conversely is useful in identifying roadway sections that do not meet minimum standards, which is more desirable than through crash reconnaissance where hydroplaning was evident. Adoption of MLS would enable the South Carolina Department of Transportation (SCDOT) to address cross-slope issues through efficient and accurate data collection methods.

Classification of Mobile Terrestrial Lidar Point Cloud in Urban Area Using Local Descriptors

“…The sensor is scanning the building, landscapes, and other features at highway speed while recording positional data using a Global Positioning System (GPS), Inertial Measurement Unit (IMU), a Distance Measurement Instrument (DMI), and digital cameras simultaneously. The resulted point cloud contains highly accurate 3D locations of highway assets and nearby areas (Shams et al, 2018). However, automated extraction of useful information from the large volume of the ponts within a point cloud has been a challenging topic in Photogrammetry and Computer Vision.…”

Utility Poles Extraction From Mobile Lidar Data in Urban Area Based on Density Information

“…There are a number of considerations when choosing a method for utility pole mapping including accuracy, safety, cost, and time. (Baffour, 2002;Shams et al, 2018;Souleyrette et al, 2003). High resolution aerial images can be accurate data sources, but manual extraction of pole locations is time consuming and conventional ground control surveying is needed to achieve high accuracy (Wen et al, 2019).…”

“…Further, weather conditions can affect accuracy (Holopainen et al, 2013). Conventional surveying is especially tedious for large areas and poses safety risks to personnel due to their proximity to traffic (Shams et al, 2018;Souleyrette et al, 2003).…”

“…Recently, the use of vehicle mounted MTLS systems has been growing for utility mapping studies (Li et al, 2019a). MTLS systems are capable of collecting dense point clouds that have many applications including transportation infrastructure mapping, asset management, Geographic Information System (GIS) data collection, and three dimensional (3D) modelling (Shams et al, 2018). An MTLS system includes a high accuracy Global Positioning System (GPS), an Inertial Measurement Unit (IMU), one or more video logging cameras, and a Distance Measuring Instrument (DMI) that is used to dead reckon between GPS points.…”

“…These applications include road surface modeling and collection of traffic signs, poles, trees, and other planimetric features in or near a roadway's right-of-way. (Balado et al, 2017;Cabo et al, 2014;Chen et al, 2018;Riveiro et al, 2015;Serna and Marcotegui, 2014;Shams et al, 2018;Xu et al, 2018;Yadav and Chousalkar, 2017;Yang et al, 2015;Yang et al, 2012). In addition, ground based MTLS collection of utility poles adjacent to the roadway results in a denser dataset compared to Airborne Laser Scanning (ALS) (Li et al, 2019a).…”