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

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.