Project description
High definition maps for self-driving cars
Autonomous vehicles (AVs) need HD maps to operate safely, as they are more detailed, accurate and reliable than conventional navigation maps. The EU funded GAMMS project is developing an autonomous terrestrial mobile mapping system (AMMS), integrating AVs, space data by Galileo, and AI technologies. Specifically, GAMMS is developing a mapping robot for geodata acquisition and an AI-based highly automated mapping software to produce HD maps from the MMS remote sensing data. GAMMS envisions fleets of low-cost, autonomous, electrically powered MMSs collecting geodata in a massive, continuous way, together with AI as a core component of an HD map processing engine to deal with huge loads of geo-data. That is, robots mapping for robots.
Objective
In GAMMS we will develop an autonomous terrestrial mobile mapping system; i.e. a mobile mapping system (MMS) robot for geodata acquisition and an AI-based highly automated mapping software. In contrast to today’s manned MMS whose cost is dominated by 2- to 3-people crews, we envision fleets of low-cost, autonomous, electrically-powered land vehicles, carrying mobile mapping systems (MMS) and collecting geodata in a massive, continuous way. Although we will develop general purpose geodata acquisition and processing techniques, in GAMMS we focus on the rapidly growing market of the High Definition (HD) maps for the autonomous vehicles (AVs), a.k.a. self-driving cars. Because of the enormous task of mapping the world roads for AVs we will develop highly automated software to produce HD maps from the MMS remote sensing data.
Because of the safety requirements of AVs, we will also develop map certification methods and quasi real-time, online techniques to continuously update the HD maps. The building blocks of GAMMS are: an electrically-powered AV, a MMS, a GNSS/Galileo receiver, multi-sensor trajectory determination software, multispectral laser scanners, vehicle dynamic models, automated mapping software and mission risk analysis methods.
A keystone of GAMMS –which encompasses the extension of the Galileo receiver and the development of ultra-safe, ubiquitous navigation methods at the 5 cm error level– is the use of Galileo features (e.g. E5 AltBOC signal) and new services: navigation message authentication (NMA), high-accuracy serive (HAS) and signal authentication. Galileo and our trajectory determination methods enable the GAMMS concept.
Our market value proposition is the production of high-accuracy high-reliable maps at a fraction of today’s cost.
Fields of science
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- natural sciencescomputer and information sciencessoftware
- social sciencessocial geographytransportnavigation systemssatellite navigation systemglobal navigation satellite system
- engineering and technologyenvironmental engineeringremote sensing
Keywords
Programme(s)
Funding Scheme
IA - Innovation actionCoordinator
08860 Castelldefels Barcelona
Spain
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.