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Precise and Robust Positioning for Automated Road Transports

PRoPART is a H2020 project, funded by the European Global Navigation Satellite System Agency (GSA), focusing on automated vehicles and advanced driver assistance systems. The main purpose of the project is to develop and enhance a GNSS RTK (Real Time Kinematic) software solution by exploiting the distinguished features of Galileo signals.

Autonomous vehicles and advanced driver assistance systems contribute towards “Vision Zero”, i.e. a future where no humans are killed or impaired by accidents. Predictions indicate that these technologies will also contribute to reduced traffic density through increased road efficiency and will create new business models for mobility. It has already been proven to reduce both the number and extent of injuries and insurance costs.

Precise and robust positioning is a required key technology in both advanced driver assistance systems and connected autonomous vehicle applications. The main idea behind the PRoPART project is to develop and enhance an RTK (Real Time Kinematic) software solution by both exploiting the distinguished features of Galileo signals as well as combining it with other positioning and sensor technologies.

No existing technology can to solve all situations

Today, there are several types of sensors used in autonomous vehicles such as cameras, laser scanners, ultrasonic, radar etc. The connected and autonomous vehicle applications currently under development are based on the cooperation between different solutions to determine the absolute position of the vehicle on the road and relative to any obstacles. No single technology has the ability to solve this in all situations and when combining different technologies, it is vital to understand the dependability of the available information.

RTK is a technique widely used for precise GNSS positioning based on the use of code and carrier phase measurements from the primary GNSS constellation(s). The use of carrier phase measurements allows cm-level accuracies at the expense of having to solve the integer ambiguity of such carrier signals, which is a sophisticated process with a certain convergence time.  The main inconvenience of the RTK technique is that it requires a reference station relatively close to the user so that the differential satellite and transmission medium errors are negligible, of which ionospheric delay is the largest contributor.

A way to partially overcome such inconvenience appears with network RTK (NRTK or virtual RTK) which uses a set of reference stations to provide correction data local to the user. In any case, RTK/NRTK approaches works well with baselines no longer than about 15 km for single frequency solutions with the required precision of autonomous vehicle applications. Where multiple GNSS frequencies are used the ionospheric error can be accounted for as it has a frequency dependent effect increasing the operational baseline length.

The PRoPART solution will fulfil the needs of the end user

By combining the innovative solutions in the current RTK SW from Waysure with features of Galileo signals from Fraunhofer solution and extending it with positioning augmentation provided by the UWB ranging solution from Ceit-IK4, PRoPART will be able to deliver an emerging solution for the future mass market of autonomous road transport. The requirements supplied by Scania and development of a collaborative autonomous lane change application using C-ITS technologies (e.g. V2X) from Commsignia and sensor data fusion tools from Baselabs will secure that the PRoPART positioning solution will fulfil the needs of the end user.



Summary

Project name

PRoPART

Status

Active

RISE role in project

Coordinator

Project start

Duration

24 months

Total budget

3 M€

Partner

RISE Measurement Science and Technology, AstaZero, Scania, Waysure, Fraunhofer IIS, Ceit-IK4, Baselabs, Commsignia

Funders

H2020 - GSA

Project website

Coordinators

External press

Stefan Nord

Contact person

Stefan Nord

Forsknings och affärsutvecklare

+46 10 516 59 31
stefan.nord@ri.se

Read more about Stefan