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TAF-BW - Real Laboratory as Enabler for Autonomous Driving

Ochs, Sven ; Fleck, Tobias; Orf, Stefan; Schotschneider, Albert; Gontscharow, Martin; Polley, Rupert; Zofka, Marc René; Viehl, Alexander; Zöllner, J. Marius 1; Simon, Kevin ORCID iD icon 2; Frey, Michael ORCID iD icon 2
1 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)
2 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Given the rapid advancement of connected and automated transportation, its applications have significantly increased. They are being studied worldwide to shape the future of mobility. Key promises are a more comfortable, efficient and socially adapted kind of mobility. As part of the EU Horizon2020 project SHared automation Operating models for Worldwide adoption (SHOW), the Karlsruhe Test Site in the Test Area Autonomous Driving Baden-Württemberg (TAF-BW) addresses aspects of scalability to overcome challenges, which have so far hindered market penetration of this future-oriented kind of mobility.
The explored services, including passenger and cargo transport, are closely linked to the daily travel requirements of road users, particularly in peri-urban areas, to cover the last mile of their journeys, connecting them to public transport. The provided high-definition maps and the smart and intelligent roadside infrastructure of TAF-BW facilitate the testing and evaluation of automated and connected vehicles and provide supervision possibilities of the operation.
This article provides a general overview of the components, which were consistently deployed on the side of the shuttle as well as roadside infrastructure, enabling the interaction between AVs and infrastructure. ... mehr


Originalveröffentlichung
DOI: 10.4271/2023-01-1909
Scopus
Zitationen: 2
Dimensions
Zitationen: 2
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 0148-7191
KITopen-ID: 1000169408
Erschienen in Mobility 4.0, Dubai, 26th - 27th Sep 2023
Veranstaltung Mobility 4.0 (2023), Dubai, Vereinigte Arabische Emirate, 26.09.2023 – 27.09.2023
Verlag SAE International
Seiten 2023-01-1909
Serie SAE Technical Paper Series ; 2023-01-1909
Vorab online veröffentlicht am 29.12.2023
Nachgewiesen in Dimensions
Scopus
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