Study on Relative Accuracy and Verification Method of High-Definition Maps for Autonomous Driving

Bibliographic Data

ID22034171
AuthorsTengfei Yu (0000-0002-0470-3710, Beijing University of Civil Engineering and Architecture), He Huang (0009-0008-2154-0694, Beijing University of Civil Engineering and Architecture, corresponding author), Nana Jiang (0000-0002-6754-4753, Beijing University of Civil Engineering and Architecture), Tri Dev Acharya (0000-0003-0886-4201, University of California, Davis)
Year2021
Volume10
Issue11
Pages761
Publication date2021-11-11
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10110761
OpenAlexW3212670215
LanguageEN
Citations received2
References cited8

High-definition maps (HDM) for autonomous driving (AD) are an important component of AD systems. HDMs accurately provide a priori information, including lane lines, and road signs, for AD systems. It is an important task to make a reasonable accuracy assessment of the HDM. The current methods for relative accuracy evaluation of general maps in the field of mapping are not fully applicable to HDMs. In this study, a method based on point set alignment and resampling is used to evaluate the relative accuracy of lane lines, and experiments are conducted based on relevant real HDM data. The results show that the relative accuracy of the lane lines is more detailed and relevant than the traditional method. This has implications for the quality control of HDM production

A priori and a posteriori · Data mining · Resampling · 3D Surveying and Cultural Heritage · Computer Science · Mathematics · Remote Sensing and LiDAR Applications · Robotics and Sensor-Based Localization · Artificial Intelligence

Tools

Open DOIOpen Access