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

Maged Gouda¹, Bruno Arantes de Achilles Mello², Karim El-Basyouny³

¹Graduate Research Assistant, Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada.

Transportation Research Record

|April 2, 2026

Summary

This study introduces an automated method using mobile Light Detection and Ranging (lidar) to map rural highway roadside clearance. The approach efficiently identifies obstacles and assesses safety compliance, improving road maintenance decisions.

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Area of Science:

Geospatial engineering
Transportation engineering
Remote sensing

Background:

Traditional manual surveying for roadside clearance is labor-intensive and costly.
Mobile lidar offers a more efficient and cost-effective data source for roadside information extraction.

Purpose of the Study:

To develop a fully automated approach for mapping and assessing roadside clearance parameters on rural highways.
To utilize mobile lidar data for enhanced efficiency and cost-effectiveness in roadside data collection.

Main Methods:

Extraction of scanning vehicle trajectory and pavement edge trajectories.
Identification of guardrails using a conical frustum method.
Voxel-based raycasting for roadside obstacle detection and location querying.
Automated checking of roadside slopes and embankment heights against design guidelines.

Main Results:

Achieved up to 98.5% accuracy in pavement edge detection.
Successfully detected all roadside obstructions in tested highway segments.
Automated identification of noncompliant locations with substandard conditions.

Conclusions:

The proposed automated lidar-based method significantly improves the mapping and inventory of roadside clearance parameters.
This technology supports informed decision-making for road maintenance and safety upgrades by analyzing infrastructure performance.
Enables efficient and accurate assessment of road safety and compliance with design standards.