Accuracy of Friction Estimation during Driving

Hans-Ulrich Kobialka, Cornelia Lex

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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.
Originalspracheenglisch
TitelConference Design of Experiments (DOE) in Engine Development
ErscheinungsortRenningen
Herausgeber (Verlag)expert verlag GmbH
Seiten190-197
ISBN (Print)978-3-8169-3217-8
PublikationsstatusVeröffentlicht - 2013
VeranstaltungConference Design of Experiments (DOE) in Engine Development - Berlin, Deutschland
Dauer: 18 Jun 201319 Jun 2013

Publikationsreihe

NameDesign of Experiments (DoE) in Engine Development
Herausgeber (Verlag)Expert Verlag

Konferenz

KonferenzConference Design of Experiments (DOE) in Engine Development
LandDeutschland
OrtBerlin
Zeitraum18/06/1319/06/13

Fingerprint

Friction
Sensors
Recurrent neural networks
Tires
Time series

Fields of Expertise

  • Mobility & Production

Treatment code (Nähere Zuordnung)

  • Application
  • Theoretical

Dies zitieren

Kobialka, H-U., & Lex, C. (2013). Accuracy of Friction Estimation during Driving. in Conference Design of Experiments (DOE) in Engine Development (S. 190-197). (Design of Experiments (DoE) in Engine Development). Renningen: expert verlag GmbH .

Accuracy of Friction Estimation during Driving. / Kobialka, Hans-Ulrich; Lex, Cornelia.

Conference Design of Experiments (DOE) in Engine Development. Renningen : expert verlag GmbH , 2013. S. 190-197 (Design of Experiments (DoE) in Engine Development).

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Kobialka, H-U & Lex, C 2013, Accuracy of Friction Estimation during Driving. in Conference Design of Experiments (DOE) in Engine Development. Design of Experiments (DoE) in Engine Development, expert verlag GmbH , Renningen, S. 190-197, Berlin, Deutschland, 18/06/13.
Kobialka H-U, Lex C. Accuracy of Friction Estimation during Driving. in Conference Design of Experiments (DOE) in Engine Development. Renningen: expert verlag GmbH . 2013. S. 190-197. (Design of Experiments (DoE) in Engine Development).
Kobialka, Hans-Ulrich ; Lex, Cornelia. / Accuracy of Friction Estimation during Driving. Conference Design of Experiments (DOE) in Engine Development. Renningen : expert verlag GmbH , 2013. S. 190-197 (Design of Experiments (DoE) in Engine Development).
@inproceedings{d57a84d616d2416299701380ea16fe62,
title = "Accuracy of Friction Estimation during Driving",
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.",
author = "Hans-Ulrich Kobialka and Cornelia Lex",
year = "2013",
language = "English",
isbn = "978-3-8169-3217-8",
series = "Design of Experiments (DoE) in Engine Development",
publisher = "expert verlag GmbH",
pages = "190--197",
booktitle = "Conference Design of Experiments (DOE) in Engine Development",
address = "Germany",

}

TY - GEN

T1 - Accuracy of Friction Estimation during Driving

AU - Kobialka, Hans-Ulrich

AU - Lex, Cornelia

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

M3 - Conference contribution

SN - 978-3-8169-3217-8

T3 - Design of Experiments (DoE) in Engine Development

SP - 190

EP - 197

BT - Conference Design of Experiments (DOE) in Engine Development

PB - expert verlag GmbH

CY - Renningen

ER -