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)