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Abstract
Accuracy of online friction estimation depends on the ability of the sensors to capture information about the current interaction between road and tire. Sensors have differ-ent characteristics and limitations, so depending on the situation their contribution varies. In this work we investigated the construction of a model that maps a driving situation (represented as sensor data time series) to the accuracy of friction estimation that can be expected for this particular situation. To train such a model from data, we used „Echo State Networks“, a method for constructing and training large Recurrent Neural Networks.
Originalsprache | englisch |
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Titel | Conference Design of Experiments (DOE) in Engine Development |
Erscheinungsort | Renningen |
Herausgeber (Verlag) | expert verlag GmbH |
Seiten | 190-197 |
ISBN (Print) | 978-3-8169-3217-8 |
Publikationsstatus | Veröffentlicht - 2013 |
Veranstaltung | Conference Design of Experiments (DOE) in Engine Development - Berlin, Deutschland Dauer: 18 Juni 2013 → 19 Juni 2013 |
Publikationsreihe
Name | Design of Experiments (DoE) in Engine Development |
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Herausgeber (Verlag) | Expert Verlag |
Konferenz
Konferenz | Conference Design of Experiments (DOE) in Engine Development |
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Land/Gebiet | Deutschland |
Ort | Berlin |
Zeitraum | 18/06/13 → 19/06/13 |
Fields of Expertise
- Mobility & Production
Treatment code (Nähere Zuordnung)
- Application
- Theoretical
Projekte
- 1 Abgeschlossen
-
FTG-S04 Ermittlung des Haftungspotentials zwischen Reifen und Straße
1/05/08 → 31/12/23
Projekt: Forschungsprojekt