Solche szenarienbasierten Simulationen mit dem Einsatz von HD-Maps im Fahrzeug stellen bereits hohe Ansprüche an Kartendaten, die entweder synthetisch generiert oder projektspezifisch, aufwändig per Mobile Mapping erfasst werden müssen. Für diese Anwendungsfälle werden HD-Maps benötigt, also hochgenaue Kartendaten des Verkehrsbereichs mit beispielsweise Informationen zu Fahrstreifengrenzen, Markierungen und Infrastruktur. Simulation auf variierenden Streckenabschnitten validiert, um später auf dem Prüfgelände in einem Fahrzeug unter Realbedingungen untersucht zu werden. Bei der "Entwicklung einer durchgängigen und flexiblen Werkzeugkette zur Absicherung des automatisierten Fahrens" spielen unter anderem Kartendaten eine tragende Rolle, denn zur Erprobung einer automatisierten Fahrfunktion wird diese vorerst in der. View full-textĭas Verbundprojekt PEGASUS befasst sich erstmals mit "Qualitätsstandards und Methoden für die Zulassung hochautomatisierter Fahrfunktionen". Based on that, open GIS frameworks are presented which enable ad hoc visualization and web-map publishing of such OpenDRIVE data. The proposed approach introduces persisting of OpenDRIVE elements in spatial databases through geometry discretisation into OGC Simple Features while maintaining the original XML data for fast subset generation and data browsing. Addressing these challenges, this presentation shows one possible approach for well-performing serving of large-scale OpenDRIVE datasets without any rocket science but with the use of standardized, well-established technologies of the geodata domain. This subset extraction is a common case because such huge datasets are seldom used in whole for a limited simulation use case. It becomes even more challenging when - according to changing requirements during project runtime - varying snippets of these datasets are to be extracted dynamically. Due to OpenDRIVE's complex data structure the plain management of such big datasets is already cumbersome. Various German and international test bed activities acquire extensive amounts of highly detailed road network data covering hundreds and thousands of kilometres of motorways, extra-urban and urban roads. In addition to prototyping of small-scale road segments for special driving simulation use cases the depiction of real-world road network datasets in OpenDRIVE becomes increasingly prominent. By bringing both domains closer together we hope to stimulate promising development of scenario generation and synthesis of reality-based road networks for driving simulator applications. This paper describes an extension of the free and open-source Geospatial Data Abstraction Library (GDAL) with OpenDRIVE as missing link between the domains of driving simulation and geographic information systems. The GIS domain provides well-established and convenient tools for spatial data processing, but does not yet offer support for OpenDRIVE data. makes generation, processing and validation of OpenDRIVE cumbersome. Such road networks, as used in driving/traffic simulation and test vehicles, are provided in specialised description formats - one of which being OpenDRIVE. Extensive and highly detailed real-world road networks obtained through mobile mapping and spatial data processing build a basis for development and evaluation of advanced driver assistant and automation systems nowadays.
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