Size-Controlled LiDAR-Detectable\nBlack Pigments: Comparative\nStudy from the Nano- to the Micron-Scale

Light detection and ranging (LiDAR) sensors emit pulsed\nnear-infrared\n(NIR) lasers toward targets and detect the reflected signals to measure\nthe distance between LiDAR-equipped autonomous vehicles and surrounding\nobjects. However, LiDAR sensors have difficulty recognizing black\nand dark objects owing to their intrinsic light absorption characteristics,\nincluding NIR light, which limits their practical application in autonomous\ndriving environments. In this study, NIR-reflective silica/black titania\ncore/shell (SBT/CS) pigments of various sizes (140 nm, 1.3 μm,\nand 6.6 μm) ranging from the nano- to micron-scale are synthesized\nto evaluate the suitability of LiDAR-detectable paints in car coatings.\nThe silica cores are fabricated using the Stöber method by\nadjusting the quantity of the reagents and then coated with a black\ntitania shell via titanium tetrachloride sol–gel and NaBH<sub>4</sub> reduction methods. In practical LiDAR applications, the SBT/CS\npigments with 100 nm cores exhibit the highest NIR reflectance (27.8 <i>R</i>% at 905 nm) owing to their high specific surface area.\nConversely, SBT/CS pigments with 6 μm cores demonstrate excellent\nspreadability and durability owing to their increased contact area\nand mechanical strength. All of the SBT/CS-painted objects are successfully\nrecognized using commercial LiDAR sensors, thus confirming the advantages\nof micron-scaled SBT/CS-based paints in autonomous driving environments.

Publication Details

Journal
Figshare
Published
2024-10-21
DOI
https://doi.org/10.1021/acsaom.4c00368.s001
Primary Topic
Technology, Environment, Urban Planning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Size-Controlled LiDAR-Detectable\nBlack Pigments: Comparative\nStudy from the Nano- to the Micron-Scale

Figshare
Technology, Environment, Urban Planning
article

Size-Controlled LiDAR-Detectable\nBlack Pigments: Comparative\nStudy from the Nano- to the Micron-Scale

article en

Abstract

Light detection and ranging (LiDAR) sensors emit pulsed\nnear-infrared\n(NIR) lasers toward targets and detect the reflected signals to measure\nthe distance between LiDAR-equipped autonomous vehicles and surrounding\nobjects. However, LiDAR sensors have difficulty recognizing black\nand dark objects owing to their intrinsic light absorption characteristics,\nincluding NIR light, which limits their practical application in autonomous\ndriving environments. In this study, NIR-reflective silica/black titania\ncore/shell (SBT/CS) pigments of various sizes (140 nm, 1.3 μm,\nand 6.6 μm) ranging from the nano- to micron-scale are synthesized\nto evaluate the suitability of LiDAR-detectable paints in car coatings.\nThe silica cores are fabricated using the Stöber method by\nadjusting the quantity of the reagents and then coated with a black\ntitania shell via titanium tetrachloride sol–gel and NaBH<sub>4</sub> reduction methods. In practical LiDAR applications, the SBT/CS\npigments with 100 nm cores exhibit the highest NIR reflectance (27.8 <i>R</i>% at 905 nm) owing to their high specific surface area.\nConversely, SBT/CS pigments with 6 μm cores demonstrate excellent\nspreadability and durability owing to their increased contact area\nand mechanical strength. All of the SBT/CS-painted objects are successfully\nrecognized using commercial LiDAR sensors, thus confirming the advantages\nof micron-scaled SBT/CS-based paints in autonomous driving environments.

Figshare
Openalex Percentile: Top 38%
Technology, Environment, Urban Planning
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.