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PRoPART

Precise and Robust Positioning for Automated Road Transports

01.01.2018 - 31.12.2020

PRoPART is an EU funded cooperation project focusing on autonomous vehicles and advanced driver assistance systems. The main idea behind the 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.

Precise and Robust Positioning for Automated Road Transports (PRoPART) has several objectives:

  • The development and demonstration of a high availability positioning solution for connected automated driving applications.
  • Develop and enhance an existing RTK (Real Time Kinematic) software solution developed by Waysure, by exploiting the distinguished features of Galileo signals as well as combining it with other positioning and sensor technologies.
  • Besides the use of vehicle on board sensors, ‘PRoPART’ will also use a low-cost Ultra Wideband (UWB) ranging solution for redundancy and robustness in areas where the coverage of GNSS is poor e.g. in tunnels or in urban canyons.
  • In order to define the correct requirements for the PRoPART combined positioning solution, a cooperative automated vehicle application will be defined and developed. The vehicle application will rely on the high availability positioning solution and use it to couple its ADAS system with V2X and aggregate information received from other connected vehicles and Road Side Units (RSU).
  • As there will be a transition period where a lot of vehicles are neither connected nor automated, solutions having high impact during low penetration are in focus. Therefore ‘PRoPART’ will implement an RSU with high precision positioning and use both UWB as well as a traffic monitoring to supply ranging, object perception and EGNSS RTK correction data via ETSI ITS-G5 to the connected automated vehicle so it can make safe decisions based on robust data.
  • This means that ‘PRoPART’ also will implement perception layer sensor fusion that uses information collected in the LDM (Local Dynamic Map) as well as information from both the on board vehicle sensors and the high availability positioning solution. We will also exploit possibilities to distribute EGNSS RTK correction data from the RSU to the vehicle.
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