Application of Mobile Terrestrial LiDAR Scanning Systems for Identification of Potential Pavement Rutting Locations

Famili, Afshin; Sarasua, Wayne; Shams, Alireza; Davis, William J.; Ogle, Jennifer

doi:10.1177/03611981211005777

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…Other proposals take advantage of the detailed point clouds obtained through MLS to analyze surfaces in detail, as shown in the work of Famili, where pavement rutting is detected by fitting a surface from which features such as aspect, slope, and plan and profile curvatures can be computed (Famili, 2020). Cai and Rasdorf (2008) concluded that LIDAR point clouds could be directly used in modeling linear objects in a 3D space instead of being interpolated into traditional elevation data.…”

Section: Related Workmentioning

confidence: 99%

Characterizing zebra crossing zones using LiDAR data

Esmorís

Vilariño

Arango

et al. 2023

Computer aided Civil Eng

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Light detection and ranging (LiDAR) scanning in urban environments leads to accurate and dense three-dimensional point clouds where the different elements in the scene can be precisely characterized. In this paper, two LiDAR-based algorithms that complement each other are proposed. The first one is a novel profiling method robust to noise and obstacles. It accurately characterizes the curvature, the slope, the height of the sidewalks, obstacles, and defects such as potholes. It was effective for 48 of 49 detected zebra crossings, even in the presence of pedestrians or vehicles in the crossing zone. The second one is a detailed quantitative summary of the state of the zebra crossing. It contains information about the location, the geometry, and the road marking. Coarse grain statistics are more prone to obstacle-related errors and are only fully reliable for 18 zebra crossings free from significant obstacles. However, all the anomalous statistics can be analyzed by looking at the associated profiles. The results can help in the maintenance of urban roads. More specifically, they can be used to improve the quality and safety of pedestrian routes.

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Section: Related Workmentioning

confidence: 99%

Characterizing zebra crossing zones using LiDAR data

Esmorís

Vilariño

Arango

et al. 2023

Computer aided Civil Eng

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“…To analyze rut depth, transverse profiles of the LIDAR point clouds are essential. In [14] transverse profiles were evaluated using partial curve mapping, Fre'chet distance, area, curve length, and dynamic time warping techniques. They came up with a novel technique to identify rutting locations by analyzing the curvature of mobile terrestrial LiDAR systems raster surfaces.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Shoulder Drop-off Prediction for Highways Using Learning-Based Methods

Khanapuri¹,

Manasreh²,

Rechtin³

et al. 2022

Preprint

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<p>One of the critical components of pavement structures are shoulders which provide lateral support to the entire pavement structure. The vertical elevation between pavement structure surface and adjacent shoulder surface results in roadway edge drop-off. Shoulder drop-off can affect the vehicle's stability and driver's ability to handle the vehicle resulting in fatalities or damage, which is also one of transportation agencies' most cited accident-related highway conditions. The maintenance staff of state transportation departments visually inspect the shoulders at near highway speeds, which might be challenging and involves human judgment, affecting the assessment's accuracy and reliability. In this work, we provide a lost-cost solution to automate the process of shoulder drop-off assessment. Our approach analyzes the point cloud data acquired from a 3D solid-state LIDAR sensor mounted on a car traveling at highway speeds. We have explored statistical, machine learning, and end-to-end deep learning methods to predict the vertical elevation of shoulder drop-off with accuracy greater than 95$\%$. We have validated the accuracy of proposed learning-based methods with actual data collected using inexpensive LIVOX LiDAR and an Astra camera along various highway sections. </p>

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“…Light detection and ranging (LiDAR) techniques may provide an efficient and practical solution for addressing this difficult challenge ( 9 – 13 ) using low altitude airborne LiDAR scanning (ALS) and mobile terrestrial LiDAR scanning (MTLS). By adopting MTLS technology, departments of transportation could potentially experience significant savings on the cost of cross slope verification, and possibly reduce contract time for commonly administered pavement rehabilitation projects by four to six months ( 8 ).…”

mentioning

confidence: 99%

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Shams

Sarasua

Russell

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Adequate water pavement surface drainage on highways is crucial in minimizing the potential of hydroplaning. Highway cross slope has a significant effect of draining water laterally from the pavement surface. Currently, field surveying techniques and other manual measurement methods are used to collect cross slope data on a limited basis in most states, despite these methods being labor intensive and exposing personnel to traffic. Furthermore, field surveying techniques cannot provide continuous data and can only be conducted at sample-based locations. This study conducted a technical evaluation of the effectiveness of airborne LiDAR (light detection and ranging) scanning and mobile terrestrial LiDAR scanning systems in measuring pavement cross slopes. Cross slope data were extracted from the LiDAR point cloud at five selected test sections using two different methods: (i) end-to-end method using elevations only from the pavement edge lines to generate the cross slope; and (ii) 0.2 ft interval point extraction along the cross-section and using a fitted linear regression line as the basis for the cross slope. Cross slopes were also measured at test section locations using conventional surveying methods and compared with LiDAR-extracted cross slopes. Results demonstrate that LiDAR methods are reliable for collecting accurate pavement cross slopes and should be considered for the purpose of cross slope verification on a braod scale such as statewide to address cross slope and pavement surface drainage issues proactively.

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Application of Mobile Terrestrial LiDAR Scanning Systems for Identification of Potential Pavement Rutting Locations

Cited by 12 publications

References 33 publications

Characterizing zebra crossing zones using LiDAR data

Characterizing zebra crossing zones using LiDAR data

Low-Cost Shoulder Drop-off Prediction for Highways Using Learning-Based Methods

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

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