Abstract
This paper proposes an automated framework that utilizes Light Detection and Ranging (LiDAR) point cloud data to map and detect road obstacles that impact drivers’ field of view at urban intersections. The framework facilitates the simulation of a driver’s field of vision to estimate the blockage percentage as they approach an intersection. Furthermore, a collision analysis is conducted to examine the relationship between poor visibility and safety. The visibility assessment was used to determine the blockage percentage as a function of intersection control type. The safety assessment indicated that intersections with limited available sight distances (ASD) exhibited an increased risk of collisions. The research also conducted a sensitivity analysis to understand the impact of the voxel size on the extraction of intersection obstacles from LiDAR datasets. The findings from this research can be used to assess the intersection without the burden of manual intervention. This would effectively support transportation agencies in identifying hazardous intersections with poor visibility and adopt policies to enhance urban intersections’ operation and safety.
Highlights
The Federal Highway Administration (FHWA) has reported that 2.4 million collisions occur at intersections every year, which accounts for about 40% of all collisions and 21.5%of traffic fatalities in the US [1]
The results show the effectiveness of Light Detection and Ranging (LiDAR) data to identify intersection sight distance (ISD) obstructions
In order to capture the impacts of a voxel in estimating the available sight distance, different voxel sizes were tested (0.1 m, 0.15 m, and 0.2 m), as shown in Figure 6a–c that show an intersection point cloud after voxelization with different voxel sizes
Summary
The Federal Highway Administration (FHWA) has reported that 2.4 million collisions occur at intersections every year, which accounts for about 40% of all collisions and 21.5%. The ISD is specified in the road design guidelines, based on conservative values of speed, deceleration rate, perception–reaction time (PRT), and gap time acceptance Such distances are sufficient to ensure that drivers have a clear vision of conflicting vehicles, such minimum distances cannot always be achieved in densely built-up urban areas, and, safety problems arise at intersections. Detecting intersection meets current guidelines, and the sight triangle is clear from obstructions in road obstacles through site visits is it generally not an efficient way to address field-assesssuch issues the urban environment. LiDAR Scans (MLS) produces road obstacles through site visits is generally not an efficient way to address such issues because this is a tedious process requiring resources mobilization, obtaining a permit from authorities, providing work zone layouts, and traffic control strategies. This information couldcould significantly help tohelp prioritize intersections sections for improvements and select cost-effective countermeasures to enhance for improvements and select cost-effective countermeasures to enhance road safety. road safety
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