A Luminance-Based Lane Marking Candidate Quality Assessment for Autonomous Driving in GIS Contexts

Pathum Rathnayaka, Young Hun Kim, In Gu Choi, Gi Chang Kim, Duk Jung Kim

2025

Abstract

The detection of lane marking candidates is crucial for autonomous vehicles and advanced driver-assistance systems ADAS as they deliver essential information for accurate lane following, localization, and route planning. Detecting these candidates becomes difficult when road conditions deteriorate or the marking paint is faded, sometimes making identification nearly impossible. While deep/machine learning (DL/ML) methods perform well in reliable detection, they often come with the need for extensive labeled datasets, substantial computational power, and intricate parameter adjustments. In this research, we present a method purely based on digital image processing to identify and evaluate lane marking candidates, thus avoiding the use of specialized reflectivity equipment (such as luminance meters) and bypassing complex DL/ML methodologies. Our pipeline initially identifies lane marking regions using a collection of image-processing techniques. It then subsequently evaluates their quality using two conditional metrics: a luminance-based contrast ratio and a white-to-black pixel ratio. Each candidate is categorized as good, bad, or ambiguous by comparing these metrics to empirically determined thresholds. Evaluations conducted on large sets of road images from conventional and urban highways in South Korea confirm the effectiveness of our proposed method. The system significantly reduces the dependence on time-consuming labor-intensive annotation, high-end hardware, and DL/ML expertise. We thus claim that our lightweight, deployable method effectively addresses a significant gap in luminance-centric lane candidate quality evaluation and can serve either as an independent solution or as a supplementary option to more sophisticated DL/ML systems in GIS and ADAS applications.

Paper Citation

in Harvard Style

Rathnayaka P., Kim Y., Choi I., Kim G. and Kim D. (2025). A Luminance-Based Lane Marking Candidate Quality Assessment for Autonomous Driving in GIS Contexts. In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM; ISBN 978-989-758-741-2, SciTePress, pages 85-94. DOI: 10.5220/0013430400003935

in Bibtex Style

@conference{gistam25,
author={Pathum Rathnayaka and Young Kim and In Choi and Gi Kim and Duk Kim},
title={A Luminance-Based Lane Marking Candidate Quality Assessment for Autonomous Driving in GIS Contexts},
booktitle={Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM},
year={2025},
pages={85-94},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013430400003935},
isbn={978-989-758-741-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM
TI - A Luminance-Based Lane Marking Candidate Quality Assessment for Autonomous Driving in GIS Contexts
SN - 978-989-758-741-2
AU - Rathnayaka P.
AU - Kim Y.
AU - Choi I.
AU - Kim G.
AU - Kim D.
PY - 2025
SP - 85
EP - 94
DO - 10.5220/0013430400003935
PB - SciTePress