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Article

Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

1
AUDI AG, Auto-Union-Straße 1, 85045 Ingolstadt, Germany
2
Geoinformatics, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(9), 3799; https://doi.org/10.3390/su12093799
Received: 30 March 2020 / Revised: 22 April 2020 / Accepted: 28 April 2020 / Published: 7 May 2020
Automated driving technologies offer the opportunity to substantially reduce the number of road accidents and fatalities. This requires the development of systems that can handle traffic scenarios more reliable than the human driver. The extreme number of traffic scenarios, though, causes enormous challenges in testing and proving the correct system functioning. Due to its efficiency and reproducibility, the test procedure will involve environment simulations to which the system under test is exposed. A combination of traffic, driving and Vulnerable Road User (VRU) simulation is therefore required for a holistic environment simulation. Since these simulators have different requirements and support various formats, a concept for integrated spatio-semantic road space modeling is proposed in this paper. For this purpose, the established standard OpenDRIVE, which describes road networks with their topology for submicroscopic driving simulation and HD maps, is combined with the internationally used semantic 3D city model standard CityGML. Both standards complement each other, and their combination opens the potentials of both application domains—automotive and 3D GIS. As a result, existing HD maps can now be used by model processing tools, enabling their transformation to the target formats of the respective simulators. Based on this, we demonstrate a distributed environment simulation with the submicroscopic driving simulator Virtual Test Drive and the pedestrian simulator MomenTUM at a sensitive crossing in the city of Ingolstadt. Both simulators are coupled at runtime and the architecture supports the integration of automated driving functions. View Full-Text
Keywords: automated driving; autonomous driving; testing; environment simulation; road space; street space; OpenDRIVE; CityGML; driving simulation; pedestrian behavior modeling automated driving; autonomous driving; testing; environment simulation; road space; street space; OpenDRIVE; CityGML; driving simulation; pedestrian behavior modeling
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MDPI and ACS Style

Schwab, B.; Beil, C.; Kolbe, T.H. Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems. Sustainability 2020, 12, 3799. https://doi.org/10.3390/su12093799

AMA Style

Schwab B, Beil C, Kolbe TH. Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems. Sustainability. 2020; 12(9):3799. https://doi.org/10.3390/su12093799

Chicago/Turabian Style

Schwab, Benedikt, Christof Beil, and Thomas H. Kolbe 2020. "Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems" Sustainability 12, no. 9: 3799. https://doi.org/10.3390/su12093799

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