Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE International Conference on Mechatronics and Automation (ICMA 2020) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 181-185 |
| Number of pages | 5 |
| ISBN (electronic) | 978-1-7281-6416-8 |
| ISBN (print) | 978-1-7281-6417-5 |
| Publication status | Published - 2020 |
| Event | 17th IEEE International Conference on Mechatronics and Automation, ICMA 2020 - Beijing, China Duration: 13 Oct 2020 → 16 Oct 2020 |
Publication series
| Name | IEEE International Conference on Mechatronics and Automation (ICMA) |
|---|---|
| ISSN (Print) | 2152-7431 |
| ISSN (electronic) | 2152-744X |
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.
Keywords
- Driver Assistance Systems, Residual Neural Network, Time Series Classification, Tire Pressure
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Control and Optimization
Cite this
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2020 IEEE International Conference on Mechatronics and Automation (ICMA 2020). Institute of Electrical and Electronics Engineers Inc., 2020. p. 181-185 9233730 (IEEE International Conference on Mechatronics and Automation (ICMA)).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Classification of Tire Pressure in a Semitrailer Using a Convolutional Neural Network
AU - Ziaukas, Zygimantas
AU - Busch, Alexander
AU - Wielitzka, Mark
AU - Ortmaier, Tobias
AU - Kobler, Jan Philipp
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Driver Assistance Systems
KW - Residual Neural Network
KW - Time Series Classification
KW - Tire Pressure
UR - http://www.scopus.com/inward/record.url?scp=85096621363&partnerID=8YFLogxK
U2 - 10.1109/ICMA49215.2020.9233730
DO - 10.1109/ICMA49215.2020.9233730
M3 - Conference contribution
AN - SCOPUS:85096621363
SN - 978-1-7281-6417-5
T3 - IEEE International Conference on Mechatronics and Automation (ICMA)
SP - 181
EP - 185
BT - 2020 IEEE International Conference on Mechatronics and Automation (ICMA 2020)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Mechatronics and Automation, ICMA 2020
Y2 - 13 October 2020 through 16 October 2020
ER -