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Forecasting key performance indicators for smart connected vehicles
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
Volvo Cars, Gothenburg, Sweden..
2022 (English)In: Advances in artificial intelligence: IBERAMIA 2022 / [ed] A C B Garcia, M Ferro, J C R Ribon, Springer, 2022, Vol. 13788, p. 414-415Conference paper, Published paper (Refereed)
Abstract [en]

As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.

Place, publisher, year, edition, pages
Springer, 2022. Vol. 13788, p. 414-415
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743, E-ISSN 1611-3349 ; 13788
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-61083ISI: 000972628400037ISBN: 978-3-031-22418-8 (print)ISBN: 978-3-031-22419-5 (electronic)OAI: oai:DiVA.org:mau-61083DiVA, id: diva2:1771063
Conference
17th Ibero-American Conference on Artificial Intelligence (IBERAMIA), NOV 23-25 2022, Cartagena de Indias, COLOMBIA
Available from: 2023-06-20 Created: 2023-06-20 Last updated: 2023-12-13Bibliographically approved

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Mihailescu, Radu-Casian

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CiteExportLink to record
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  • apa
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