Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2023 62nd IEEE Conference on Decision and Control, CDC 2023 |
| Seiten | 5331-5338 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9798350301243 |
| Publikationsstatus | Veröffentlicht - 13 Dez. 2023 |
Publikationsreihe
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| ISSN (Print) | 0743-1546 |
| ISSN (elektronisch) | 2576-2370 |
Abstract
For the optimization of advanced driver assistance systems (ADAS) and the implementation of autonomous driving, the perception of the vehicles environment and in particular the maximum friction coefficient μ max is crucial. Since μ max cannot be measured directly via existing serial sensors, estimating this coefficient based on available sensors is an area of research. In this paper, μ max estimation is presented using transformer neural networks (TNN) based on the input data measured by onboard vehicle sensors. The TNN is applied to both a simulative dataset created with IPG CarMaker and an experimental dataset recorded on a test track, each using a sports utility vehicle (SUV) as the test vehicle. Both datasets contain typical longitudinal and lateral driving maneuvers on different road surfaces. On an independent test dataset, the data-based TNN approach shows improved results in estimating μ max compared to the model-based approach of an unscented Kalman filter (UKF) and to two other data-based approaches using recurrent artificial neural networks (RANN s) from previous works. In particular, the TNN responds faster and more accurate to jumps of μ max, especially during lateral driving maneuvers. Moreover, the TNN has both less parameters, and training epochs compared to the RANN.
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2023 62nd IEEE Conference on Decision and Control, CDC 2023. 2023. S. 5331-5338 (Proceedings of the IEEE Conference on Decision and Control).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Transformer Neural Networks for Maximum Friction Coefficient Estimation of Tire-Road Contact using Onboard Vehicle Sensors
AU - Schäfke, Hendrik
AU - Lampe, Nicolas
AU - Kortmann, Karl-Philipp
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023/12/13
Y1 - 2023/12/13
N2 - For the optimization of advanced driver assistance systems (ADAS) and the implementation of autonomous driving, the perception of the vehicles environment and in particular the maximum friction coefficient μ max is crucial. Since μ max cannot be measured directly via existing serial sensors, estimating this coefficient based on available sensors is an area of research. In this paper, μ max estimation is presented using transformer neural networks (TNN) based on the input data measured by onboard vehicle sensors. The TNN is applied to both a simulative dataset created with IPG CarMaker and an experimental dataset recorded on a test track, each using a sports utility vehicle (SUV) as the test vehicle. Both datasets contain typical longitudinal and lateral driving maneuvers on different road surfaces. On an independent test dataset, the data-based TNN approach shows improved results in estimating μ max compared to the model-based approach of an unscented Kalman filter (UKF) and to two other data-based approaches using recurrent artificial neural networks (RANN s) from previous works. In particular, the TNN responds faster and more accurate to jumps of μ max, especially during lateral driving maneuvers. Moreover, the TNN has both less parameters, and training epochs compared to the RANN.
AB - For the optimization of advanced driver assistance systems (ADAS) and the implementation of autonomous driving, the perception of the vehicles environment and in particular the maximum friction coefficient μ max is crucial. Since μ max cannot be measured directly via existing serial sensors, estimating this coefficient based on available sensors is an area of research. In this paper, μ max estimation is presented using transformer neural networks (TNN) based on the input data measured by onboard vehicle sensors. The TNN is applied to both a simulative dataset created with IPG CarMaker and an experimental dataset recorded on a test track, each using a sports utility vehicle (SUV) as the test vehicle. Both datasets contain typical longitudinal and lateral driving maneuvers on different road surfaces. On an independent test dataset, the data-based TNN approach shows improved results in estimating μ max compared to the model-based approach of an unscented Kalman filter (UKF) and to two other data-based approaches using recurrent artificial neural networks (RANN s) from previous works. In particular, the TNN responds faster and more accurate to jumps of μ max, especially during lateral driving maneuvers. Moreover, the TNN has both less parameters, and training epochs compared to the RANN.
UR - http://www.scopus.com/inward/record.url?scp=85184818711&partnerID=8YFLogxK
U2 - 10.1109/cdc49753.2023.10384175
DO - 10.1109/cdc49753.2023.10384175
M3 - Conference contribution
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5331
EP - 5338
BT - 2023 62nd IEEE Conference on Decision and Control, CDC 2023
ER -