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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images.

Ivan Brkić¹, Mario Miler¹, Marko Ševrović²

¹Department of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia.

Sensors (Basel, Switzerland)

|July 28, 2022

Summary

A new framework automates road safety assessment using Lidar and deep learning. It accurately classifies roadside objects and distances, crucial for meeting UN road safety standards by 2030.

Keywords:

Lidar road assessment road safety roadside features

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Area of Science:

Road safety engineering
Computer vision
Geospatial data analysis

Background:

United Nations (UN) mandates 3+ star road standards by 2030.
International Road Assessment Program (iRAP) uses 64 attributes for road ratings.
Accurate assessment of roadside hazards is critical for road safety.

Purpose of the Study:

To develop a framework for automatic determination of roadside severity-object and roadside severity-distance.
To integrate mobile Lidar point clouds with deep learning for road attribute classification.
To enhance the accuracy and efficiency of road safety assessments.

Main Methods:

Utilized mobile Lidar point clouds and deep learning (You Only Look Once - YOLO) for object detection.
Processed Lidar data from Croatian highways in .las format, segmented into 10m lengths.
Determined roadside severity-distance relative to the detected road edge.

Main Results:

Achieved 85.1% overall accuracy for roadside severity-object classification.
Achieved 85.6% overall accuracy for roadside severity-distance classification.
High average precision (0.98) for safety barrier concrete; lower for rockface (0.72).

Conclusions:

The proposed framework offers a highly accurate and automated solution for road safety attribute determination.
This method supports the UN's goal of improving global road infrastructure safety.
Integration of Lidar and deep learning provides a robust approach for iRAP compliance.