Automated Object Detection, Mapping, and Assessment of Roadside Clear Zones Using Lidar Data

This paper proposes a fully automated approach to map and assess roadside clearance parameters using mobile Light Detection Ranging (lidar) data on rural highways. Compared with traditional manual surveying methods, lidar could provide more efficient cost-effective source extract information. study novel voxel-based raycasting focused primarily automating mapping assessment. First, the scanning vehicle trajectory is extracted. Pavement surface points are then detected, method proposed pavement edge trajectories. Once edges extracted, guardrails were identified conical frustum emitted from points. Target flexion generated located roadside, used search for obstacles query their locations. Finally, slopes embankment heights mapped at specific intervals, design guidelines requirements automatically checked against results. Noncompliant locations substandard conditions queried. The was tested four highway segments in Alberta, Canada. accuracy of detection reached up 98.5%. Furthermore, proved be accurate object detection, being able detect all obstructions each segment. can help transportation authorities inventory parameters. Moreover, safety performance existing road infrastructure studied collected information crash support decision making maintenance upgrades.