Assessing Stopping and Passing Sight Distance on Highways Using Mobile LiDAR Data

Gargoum, Suliman; El-Basyouny, Karim; Sabbagh, J.

doi:10.1061/(asce)cp.1943-5487.0000753

Cited by 44 publications

(21 citation statements)

References 16 publications

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“…Over the past five years, mobile laser scanning (MLS) data have been recognized as a reliable data source for conducting visibility-related analyses [15][16][17][18]. MLS point clouds enable a very accurate and precise representation of real-world environment.…”

Section: Overview Of Visibility Modelingmentioning

confidence: 99%

Visibility-Based Technologies and Methodologies for Autonomous Driving

Easa¹,

Ma²,

Elshorbagy³

et al. 2021

Artificial Intelligence

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The three main elements of autonomous vehicles (AV) are orientation, visibility, and decision. This chapter presents an overview of the implementation of visibility-based technologies and methodologies. The chapter first presents two fundamental aspects that are necessary for understanding the main contents. The first aspect is highway geometric design as it relates to sight distance and highway alignment. The second aspect is mathematical basics, including coordinate transformation and visual space segmentation. Details on the Light Detection and Ranging (Lidar) system, which represents the ‘eye’ of the AV are presented. In particular, a new Lidar 3D mapping system, that can be operated on different platforms and modes for a new mapping scheme is described. The visibility methodologies include two types. Infrastructure visibility mainly addresses high-precision maps and sight obstacle detection. Traffic visibility (vehicles, pedestrians, and cyclists) addresses identification of critical positions and visibility estimation. Then, an overview of the decision element (path planning and intelligent car-following) for the movement of AV is presented. The chapter provides important information for researchers and therefore should help to advance road safety for autonomous vehicles.

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Section: Overview Of Visibility Modelingmentioning

confidence: 99%

Visibility-Based Technologies and Methodologies for Autonomous Driving

Easa¹,

Ma²,

Elshorbagy³

et al. 2021

Artificial Intelligence

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“…In a more recent study, Gargoum et al introduced a semi-automated method utilizing ArcGIS tools to compute the sight distance on highway segments using mobile LiDAR data ( 5 ). A Microsoft Visual Basic code was then written to calculate the sight distance from the ArcGIS outputs.…”

Section: Previous Studiesmentioning

confidence: 99%

“…In fact, researchers found that limited sight distance has a huge impact on the occurrence of road collisions ( 3, 4 ). Although designing highways that meet the sight distance requirements of design guides, in theory, ensures that sufficient sight distance will be available along the highway, the presence of constraints (e.g., practical or financial) during the design and construction stages may lead to insufficient sight distance at some locations ( 5 ). In addition, highway rehabilitation and pavement maintenance work during the road’s service life could result in changes to the original road design, altering the ASD at certain locations.…”

mentioning

confidence: 99%

Voxel-Based Methodology for Automated 3D Sight Distance Assessment on Highways using Mobile Light Detection and Ranging Data

Shalkamy

El-Basyouny

2020

Transportation Research Record: Journal of the Transportation R

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Ensuring that the available sight distance (ASD) on highways meets the minimum requirements of geometric design standards is crucial for safe and efficient operation of highways. Current practices of ASD assessment using design software or through site visits are labor intensive, time consuming, and traffic disruptive. Thus, this paper introduces a fully automated algorithm that allows large-scale assessment of ASD in three-dimensional (3D) space on highways utilizing mobile light detection and ranging (LiDAR) data. The algorithm was tested on LiDAR data of highway segments in Alberta, Canada. The results showed that the algorithm was highly accurate in detecting sight distance limitations at the defined regions and, in all cases, the driver’s vision was restricted by the pavement surface on vertical crest curves. In the case of combined vertical and horizontal curves, the vertical crest curve was found to be the controlling element in sight distance deficiencies. In addition, the assessment of historical collision data revealed clusters along the regions defined with ASD limitations, indicating that restrictions in drivers’ vision could have contributed to the collision occurrence.

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“…Whereas digital terrain models (DTMs) represent the bare ground surface, digital surface models (DSMs) reproduce additional surfaces on the terrain such as vegetation or buildings. Several procedures have been devised in geographic information systems (GIS) to assess sight distance on existing highways, which reproduce the DTM by means of a either a raster file [15,16] or a triangular irregular network [17].…”

Section: Introductionmentioning

confidence: 99%

Using Small Unmanned Aerial Vehicle in 3D Modeling of Highways with Tree-Covered Roadsides to Estimate Sight Distance

et al. 2019

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The safe and efficient operation of highways largely depends on the adequate provision of sight distance. Small unmanned aerial vehicles (UAVs) can be utilized to efficiently complete data acquisition very soon after identifying an issue when searching for potential highway safety risks. A double grid flight is proposed to obtain an adequate three-dimensional (3D) recreation of the road environment, ensuring an unbiased sight distance output. Then, a dense cloud point is derived through a Structure from Motion Multi-View Stereo process. The point cloud is classified to produce both a terrain model, characterized by its resolution, and a 3D-object model, characterized by the maximum edge length of the entities. The resulting road environment model is utilized to calculate sight distance in a geographic information system. The results enabled the detection of accident-prone locations caused by sight distance limitations. Moreover, the impact of the 3D modeling parameters on the results was assessed.

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Assessing Stopping and Passing Sight Distance on Highways Using Mobile LiDAR Data

Cited by 44 publications

References 16 publications

Visibility-Based Technologies and Methodologies for Autonomous Driving

Visibility-Based Technologies and Methodologies for Autonomous Driving

Voxel-Based Methodology for Automated 3D Sight Distance Assessment on Highways using Mobile Light Detection and Ranging Data

Using Small Unmanned Aerial Vehicle in 3D Modeling of Highways with Tree-Covered Roadsides to Estimate Sight Distance

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