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Autoshuttle: A Novel Dataset for Advancing Autonomous Driving in Shuttle-Specific Environments
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Zhou, Lixian; Salaar, Hamza; Schmidt, Michael; Dehghani, Ali; Arbeiter, Georg (2024): Autoshuttle: A Novel Dataset for Advancing Autonomous Driving in Shuttle-Specific Environments. 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI) 2024, S. 383-390. DOI: 10.1109/SAMI60510.2024.10432901
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The website of Coburg University of Applied Sciences was translated using translation software provided by a third-party provider such as DeepL. The official text is the German version of the website. No liability is assumed, either explicitly or implicitly, for the accuracy, reliability, or correctness of the translations into another language.

Autoshuttle: A Novel Dataset for Advancing Autonomous Driving in Shuttle-Specific Environments

Titel:

Autoshuttle: A Novel Dataset for Advancing Autonomous Driving in Shuttle-Specific Environments

Veröffentlichungsdatum:

26.01.2024

Publikationsart:

Beitrag zu wissenschaftlicher Konferenz/Tagung

Forschungsschwerpunkt:

kein FSP zugeordnet

Medien:

2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)

DOI:

https://doi.org/10.1109/SAMI60510.2024.10432901

Weblink:

Heft:

Band:

2024

Artikelnummer:

ISBN:

Autoren:

Lixian Zhou, Hamza Salaar, Michael Schmidt, Ali Dehghani, Georg Arbeiter

Medien:

2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)

Herausgeber:

IEEE

Seiten:

383-390

Open Access:

Peer reviewed:

Ja

Zitierung:

Zhou, Lixian; Salaar, Hamza; Schmidt, Michael; Dehghani, Ali; Arbeiter, Georg (2024): Autoshuttle: A Novel Dataset for Advancing Autonomous Driving in Shuttle-Specific Environments. 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI) 2024, S. 383-390. DOI: 10.1109/SAMI60510.2024.10432901

Autoren:

Lixian Zhou, Hamza Salaar, Michael Schmidt, Ali Dehghani, Georg Arbeiter