Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2024 European Control Conference, ECC 2024 |
| Seiten | 456-463 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 978-3-9071-4410-7 |
| Publikationsstatus | Veröffentlicht - 2024 |
| Veranstaltung | 2024 European Control Conference (ECC) - Stockholm, Schweden Dauer: 25 Juni 2024 → 28 Juni 2024 |
Abstract
Advanced driver assistance systems are critically dependent on reliable and accurate information regarding a vehicles' driving state. For estimation of unknown quantities, model-based and learning-based methods exist, but both suffer from individual limitations. On the one hand, model-based estimation performance is often limited by the models' accuracy. On the other hand, learning-based estimators usually do not perform well in 'unknown' conditions (bad generalization), which is particularly critical for semitrailers as their payload changes significantly in operation. To the best of the authors' knowledge, this work is the first to analyze the capability of state-of-the-art estimators for semitrailers to generalize across 'unknown' loading states. Moreover, a novel hybrid Extended Kalman Filter (H-EKF) that takes advantage of accurate Artificial Neural Network (ANN) estimates while preserving reliable generalization capability is presented. It estimates the articulation angle between truck and semitrailer, lateral tire forces and the truck steering angle utilizing sensor data of a standard semitrailer only. An experimental comparison based on a full-scale truck-semitrailer combination indicates the superiority of the H-EKF compared to a state-of-the-art Extended Kalman Filter and an ANN estimator.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Steuerung und Optimierung
- Mathematik (insg.)
- Modellierung und Simulation
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2024 European Control Conference, ECC 2024. 2024. S. 456-463.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Reliable State Estimation in a Truck-Semitrailer Combination using an Artificial Neural Network-Aided Extended Kalman Filter
AU - Ewering, Jan-Hendrik
AU - Ziaukas, Zygimantas
AU - Ehlers, Simon Friedrich Gerhard
AU - Seel, Thomas
N1 - Publisher Copyright: © 2024 EUCA.
PY - 2024
Y1 - 2024
N2 - Advanced driver assistance systems are critically dependent on reliable and accurate information regarding a vehicles' driving state. For estimation of unknown quantities, model-based and learning-based methods exist, but both suffer from individual limitations. On the one hand, model-based estimation performance is often limited by the models' accuracy. On the other hand, learning-based estimators usually do not perform well in 'unknown' conditions (bad generalization), which is particularly critical for semitrailers as their payload changes significantly in operation. To the best of the authors' knowledge, this work is the first to analyze the capability of state-of-the-art estimators for semitrailers to generalize across 'unknown' loading states. Moreover, a novel hybrid Extended Kalman Filter (H-EKF) that takes advantage of accurate Artificial Neural Network (ANN) estimates while preserving reliable generalization capability is presented. It estimates the articulation angle between truck and semitrailer, lateral tire forces and the truck steering angle utilizing sensor data of a standard semitrailer only. An experimental comparison based on a full-scale truck-semitrailer combination indicates the superiority of the H-EKF compared to a state-of-the-art Extended Kalman Filter and an ANN estimator.
AB - Advanced driver assistance systems are critically dependent on reliable and accurate information regarding a vehicles' driving state. For estimation of unknown quantities, model-based and learning-based methods exist, but both suffer from individual limitations. On the one hand, model-based estimation performance is often limited by the models' accuracy. On the other hand, learning-based estimators usually do not perform well in 'unknown' conditions (bad generalization), which is particularly critical for semitrailers as their payload changes significantly in operation. To the best of the authors' knowledge, this work is the first to analyze the capability of state-of-the-art estimators for semitrailers to generalize across 'unknown' loading states. Moreover, a novel hybrid Extended Kalman Filter (H-EKF) that takes advantage of accurate Artificial Neural Network (ANN) estimates while preserving reliable generalization capability is presented. It estimates the articulation angle between truck and semitrailer, lateral tire forces and the truck steering angle utilizing sensor data of a standard semitrailer only. An experimental comparison based on a full-scale truck-semitrailer combination indicates the superiority of the H-EKF compared to a state-of-the-art Extended Kalman Filter and an ANN estimator.
UR - http://www.scopus.com/inward/record.url?scp=85200579334&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2406.14028
DO - 10.48550/arXiv.2406.14028
M3 - Conference contribution
SN - 979-8-3315-4092-0
SP - 456
EP - 463
BT - 2024 European Control Conference, ECC 2024
T2 - 2024 European Control Conference (ECC)
Y2 - 25 June 2024 through 28 June 2024
ER -