Temporary Road Marking Paint for Vehicle Perception Tests

In order to test camera- and LiDAR-based perception of road markings for automated driving, vehicle developers have started to utilize concepts for the agile alteration of road marking patterns on proving grounds. Road marking materials commonly used within this concept are different types of tape that can easily be applied and removed on asphalt and concrete. Due to the elasticity of tape, it cannot be used efficiently for small radii, symbols, lettering, and specific corner shapes (e.g., for parking slots). These road marking patterns are common in urban environments. With the growing capability of automated driving systems and more applications for urban environments, edgy road marking shapes gain importance for proving ground testing. This study examines the use of water-soluble road marking paint specifically designed for the use case of temporary applications on proving grounds for camera- and LiDAR-based perception testing. We found that white, water-soluble paint with 1.5% binder content and 2.25% coalescing agent content can provide realistic road markings for vehicle testing purposes. However, solubility affects the paint’s vulnerability to fading during rain. Hence, renewal activities over the course of longer test drives might be necessary. The paint could be removed using water pressure without significant residue or damaging of the asphalt.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2024-08-21
DOI
https://doi.org/10.3390/app14167362
Citations
3
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Temporary Road Marking Paint for Vehicle Perception Tests

Nils Katzorke, Lara Schunggart, Lisa-Marie Langwaldt
3 citations
Applied Sciences
Autonomous Vehicle Technology and Safety
0.61
article

Temporary Road Marking Paint for Vehicle Perception Tests

Nils Katzorke, Lara Schunggart, Lisa-Marie Langwaldt
article en
3 citations

Abstract

In order to test camera- and LiDAR-based perception of road markings for automated driving, vehicle developers have started to utilize concepts for the agile alteration of road marking patterns on proving grounds. Road marking materials commonly used within this concept are different types of tape that can easily be applied and removed on asphalt and concrete. Due to the elasticity of tape, it cannot be used efficiently for small radii, symbols, lettering, and specific corner shapes (e.g., for parking slots). These road marking patterns are common in urban environments. With the growing capability of automated driving systems and more applications for urban environments, edgy road marking shapes gain importance for proving ground testing. This study examines the use of water-soluble road marking paint specifically designed for the use case of temporary applications on proving grounds for camera- and LiDAR-based perception testing. We found that white, water-soluble paint with 1.5% binder content and 2.25% coalescing agent content can provide realistic road markings for vehicle testing purposes. However, solubility affects the paint’s vulnerability to fading during rain. Hence, renewal activities over the course of longer test drives might be necessary. The paint could be removed using water pressure without significant residue or damaging of the asphalt.

Applied SciencesVol. 14(16)
Mercedes-Benz (Germany) (DE), Furtwangen University (DE)
Sustainable cities and communities
Openalex Percentile: Top 34%
Autonomous Vehicle Technology and Safety
0.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.