To realize the societal need for greener, safer, and smarter mobility, ambitious technical challenges need to be addressed. With this aim, the H2020-EUSPA project ESRIUM investigates various aspects of highly accurate, reliable, and assured EGNSS localization information for road vehicles with a particular focus on automated vehicles. To analyze the achievable accuracy, reliability, and availability of multi-frequency and multi-GNSS mass-market receivers, we have conducted test drives under different GNSS reception conditions. In the tests, special focus was placed on using the Galileo Open Service Navigation Message Authentication (OSNMA) service, offering an additional feature for assured PVT (position, velocity, and time) information with respect to spoofing. We analyzed the performance of three Septentrio Mosaic-X5 receivers operated with different OSNMA settings. It could be shown that strict use of OSNMA provides very good positioning accuracy as long as sufficient suitable satellites are available. However, the overall performance suffers from a reduced satellite number and is therefore limited. The performance of a receiver using authenticated Galileo with GPS signals (final status of Galileo OSNMA) is very good for a mass-market receiver: 92.55% of the solutions had a 2D position error below 20 cm during 8.5 h of driving through different environments.

High-accuracy GNSS (Global Navigation Satellite System) positioning requires the receiver to use correction data. This data is typically delivered via 4G mobile internet. In this paper, we present a novel method to deliver the data via C-ITS (Cooperative Intelligent Transport Systems and Service). We compare its performance against using 4G and analyze the impact on the accuracy during data gaps, all using data collected in test drives from a real deployment in a small segment of a motorway. The results show that with C-ITS, a comparable performance can be achieved. The observed 2D position errors in our tests were below 3.1 cm for 95% and below 10 cm for more than 99% of the time.