Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies-Reference-Cited by-同舟云学术

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)
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Author:

Hernangómez Rodrigo¹,Geuer Philipp²,Palaios Alexandros²,Schäufele Daniel¹,Watermann Cara²,Taleb-Bouhemadi Khawla¹,Parvini Mohammad³,Krause Anton³,Partani Sanket⁴,Vielhaus Christian⁵,Kasparick Martin¹,Külzer Daniel F.⁶,Burmeister Friedrich³,Fitzek Frank H. P.⁵,Schotten Hans D.⁴,Fettweis Gerhard³,Stańczak Sławomir¹

Affiliation:

1. Fraunhofer Heinrich Hertz Institute,Germany

2. Ericsson Research,Germany

3. Vodafone Chair, Technische Universität Dresden,Germany

4. Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau,Germany

5. Technische Universität Dresden,Deutsche Telekom Chair,Germany

6. BMW Group,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10199114/10199145/10200750.pdf?arnumber=10200750

Reference20 articles.

1. CRAWDAD dataset gat-ech/vehicular (v. 2006-03-15);fujimoto,2006

2. Cooperative localization in vehicular networks dataset;cruz,2020

3. Neighbor-Aided Localization in Vehicular Networks

4. From Paris to London: Comparative analysis of licensed spectrum use in two European metropolises

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