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Forschung
Institute for Data and Process Science

5-Safe: AI-Based Road Safety Enhancement for Schoolchildren Using 5G

Autoren

Prof. Dr. Abdelmajid Khelil
Abdelmajid.Khelil@haw-landshut.de
Prof. Dr. Holger Timinger
Holger.Timinger@haw-landshut.de
Dominic Scholze
Dominic.Scholze@haw-landshut.de
Marcel Mueller
Marcel.Mueller@haw-landshut.de
Prof. Dr. Bettina Kühbeck
Bettina.Kuehbeck@haw-landshut.de
Ahmed Chebaane
Ahmed.Chebaane.1@haw-landshut.de
Tobias Piller
Abdullah Al-Khatib
Michael Weber
Ranothan Ravichandran
Tim Laine

Medien

IEEE E-TEMS 2023

Veröffentlichungsjahr

2023

Veröffentlichungsart

Konferenzbeitrag (peer reviewed)

Zitierung

Scholze, Dominic; Al-Khatib, Abdullah; Chebaane, Ahmed; Mueller, Marcel; Ziegler, Tobias; Ravichandran, Ranothan; Luger, Michael; Laine, Tim; Khelil, Abdelmajid; Timinger, Holger; Kuehbeck, Bettina (2023): 5-Safe: AI-Based Road Safety Enhancement for Schoolchildren Using 5G. IEEE E-TEMS 2023.

Peer Reviewed

Ja

Institute for Data and Process Science

5-Safe: AI-Based Road Safety Enhancement for Schoolchildren Using 5G

Abstract

In this paper, we focus on the planning phase of the project 5-Safe outlining four use cases that can improve school route safety in a smart city. The project investigates how 5G mobile communications infrastructure and machine learning based sensor data processing can reduce road traffic accidents. We describe the potential impact of the measures on school route safety by a network of multi-modal sensors and actors in front of three schools.