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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Schwab, Benedikt; Beil, Christof; Kolbe Thomas H.
Abstract:
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 ex...     »
Stichworte:
GISPro_CityGML; GISTop_CityModeling; LOCenter; LOCTop_Urban_Information_Modeling_Virtual_3D_City_Model; GISPro_SAVe
Zeitschriftentitel:
Sustainability
Jahr:
2020
Band / Volume:
12
Jahr / Monat:
2020-05
Quartal:
2. Quartal
Monat:
May
Heft / Issue:
9
Seitenangaben Beitrag:
3799
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.3390/su12093799
WWW:
https://www.mdpi.com/2071-1050/12/9/3799
Verlagsort:
MDPI
E-ISSN:
2071-1050
Status:
Verlagsversion / published
Eingereicht (bei Zeitschrift):
30.03.2020
Angenommen (von Zeitschrift):
28.04.2020
Publikationsdatum:
07.05.2020
Semester:
SS 20
TUM Einrichtung:
Lehrstuhl für Geoinformatik
CC-Lizenz:
by, http://creativecommons.org/licenses/by/4.0
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