Classification of Tire Pressure in a Semitrailer Using a Convolutional Neural Network

verfasst von
Zygimantas Ziaukas, Alexander Busch, Mark Wielitzka, Tobias Ortmaier, Jan Philipp Kobler
Abstract

Test Online information about states and parameters of passive vehicles (e.g. trailers) is of high importance for the future of automotive driving and has been quite neglected until today. Direct measurements of these states may require costly additional hardware which costumers are not often willing to pay for. Therefore, in this paper a method for the classification of tire pressure for one tire of a commercial vehicle's semitrailer is presented. The classification is based on measurement of the adjoining axle's vertical acceleration and the wheel speed using a Residual Neural Network (ResNet). The tire pressure is divided into three classes of 8.5 bar, 7.0 bar and 5.5 bar. The experimental results show accuracies beyond 90% for the test case.

Organisationseinheit(en)
Institut für Mechatronische Systeme
Externe Organisation(en)
BPW Bergische Achsen KG
Typ
Aufsatz in Konferenzband
Seiten
181-185
Anzahl der Seiten
5
Publikationsdatum
2020
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Artificial intelligence, Computernetzwerke und -kommunikation, Angewandte Informatik, Elektrotechnik und Elektronik, Maschinenbau, Steuerung und Optimierung
Elektronische Version(en)
https://doi.org/10.1109/ICMA49215.2020.9233730 (Zugang: Geschlossen)