Advancements in Enhancing GNSS RTK Positioning Accuracy and Integrity for Automated Driving

authored by: Steffen Schön, Kai-Niklas Baasch, Ali KarimiDoona, Dennis Kulemann, Fabian Ruwisch, Anat Schaper, Jingyao Su
Abstract: For safety-critical applications like autonomous driving, high trust in the navigation solution is essential, primarily measured by integrity. Multipath and further propagation specific errors in GNSS observations present significant challenges, as they can only be partially corrected. To ensure high integrity in urban navigation, it is crucial to understand the signal propagation mechanisms and potential error sources in these complex environments. Our group has made recent progress in this area, conducting various experiments in urban areas to analyze GNSS positioning performance. Using ray tracing, GNSS channel models, and 3D city models, the signal propagation conditions can be classified and errors quantified. We create GNSS Feature Maps to analyse the spatio-temporal similarity of the geometry-related error features and developed a Feature Map aided robust GNSS RTK algorithm, yielding improved accuracy and fulfilling our newly defined alert limits for German roads. We show how collaborative positioning can further improve this situation.
Organisation(s): Institute of Geodesy
Faculty of Civil Engineering and Geodetic Science
Type: Conference contribution
Pages: 460-467
No. of pages: 8
Publication date: 22.06.2025
Publication status: Published
Peer reviewed: Yes
Electronic version(s): https://doi.org/10.1109/iv64158.2025.11097807 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@inproceedings{ca5162b405a34c3c877b54f26bec2e1a,
title = "Advancements in Enhancing GNSS RTK Positioning Accuracy and Integrity for Automated Driving",
abstract = "For safety-critical applications like autonomous driving, high trust in the navigation solution is essential, primarily measured by integrity. Multipath and further propagation specific errors in GNSS observations present significant challenges, as they can only be partially corrected. To ensure high integrity in urban navigation, it is crucial to understand the signal propagation mechanisms and potential error sources in these complex environments. Our group has made recent progress in this area, conducting various experiments in urban areas to analyze GNSS positioning performance. Using ray tracing, GNSS channel models, and 3D city models, the signal propagation conditions can be classified and errors quantified. We create GNSS Feature Maps to analyse the spatio-temporal similarity of the geometry-related error features and developed a Feature Map aided robust GNSS RTK algorithm, yielding improved accuracy and fulfilling our newly defined alert limits for German roads. We show how collaborative positioning can further improve this situation.",
author = "Steffen Sch{\"o}n and Kai-Niklas Baasch and Ali KarimiDoona and Dennis Kulemann and Fabian Ruwisch and Anat Schaper and Jingyao Su",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.",
year = "2025",
month = jun,
day = "22",
doi = "10.1109/iv64158.2025.11097807",
language = "English",
isbn = "979-8-3315-3804-0",
series = "IEEE Intelligent Vehicles Symposium, Proceedings",
pages = "460--467",
booktitle = "2025 IEEE Intelligent Vehicles Symposium (IV)",
}