Details
| Original language | English |
|---|---|
| Title of host publication | 2018 Annual American Control Conference, ACC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2593-2598 |
| Number of pages | 6 |
| ISBN (print) | 9781538654286 |
| Publication status | Published - 9 Aug 2018 |
| Event | 2018 Annual American Control Conference, ACC 2018 - Milwauke, United States Duration: 27 Jun 2018 → 29 Jun 2018 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| Volume | 2018-June |
| ISSN (Print) | 0743-1619 |
Abstract
Automotive research and development passed through an enormous development within the last decades. In particular focusing on the goal of autonomous driving an tremendous potential is expected in the upcoming years. In this regard a robust and exact perception of the vehicle's environment is necessary. Especially, the road condition, represented by the friction coefficient between tires and road, has major influence on the vehicle's behavior and thus its stability. Therefore, robust online estimation of the friction coefficient is indispensable for autonomous driving to ensure save driving. In this paper online estimation of the bounded maximum friction coefficient based on serial sensors is presented using a sensitivity-based joint unscented Kalman filter. To achieve robust estimation without parameter estimation drift during phases of insufficient excitation, a local sensitivity analysis is introduced. The friction estimation results are validated by utilizing measurements taken on a Volkswagen Golf GTE Plug-In Hybrid on four different road surfaces for longitudinal and lateral dynamic maneuvers.
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
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2018 Annual American Control Conference, ACC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2593-2598 8431259 (Proceedings of the American Control Conference; Vol. 2018-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Sensitivity-based Road Friction Estimation in Vehicle Dynamics using the Unscented Kalman Filter
AU - Wielitzka, Mark
AU - Dagen, Matthias
AU - Ortmaier, Tobias
PY - 2018/8/9
Y1 - 2018/8/9
N2 - Automotive research and development passed through an enormous development within the last decades. In particular focusing on the goal of autonomous driving an tremendous potential is expected in the upcoming years. In this regard a robust and exact perception of the vehicle's environment is necessary. Especially, the road condition, represented by the friction coefficient between tires and road, has major influence on the vehicle's behavior and thus its stability. Therefore, robust online estimation of the friction coefficient is indispensable for autonomous driving to ensure save driving. In this paper online estimation of the bounded maximum friction coefficient based on serial sensors is presented using a sensitivity-based joint unscented Kalman filter. To achieve robust estimation without parameter estimation drift during phases of insufficient excitation, a local sensitivity analysis is introduced. The friction estimation results are validated by utilizing measurements taken on a Volkswagen Golf GTE Plug-In Hybrid on four different road surfaces for longitudinal and lateral dynamic maneuvers.
AB - Automotive research and development passed through an enormous development within the last decades. In particular focusing on the goal of autonomous driving an tremendous potential is expected in the upcoming years. In this regard a robust and exact perception of the vehicle's environment is necessary. Especially, the road condition, represented by the friction coefficient between tires and road, has major influence on the vehicle's behavior and thus its stability. Therefore, robust online estimation of the friction coefficient is indispensable for autonomous driving to ensure save driving. In this paper online estimation of the bounded maximum friction coefficient based on serial sensors is presented using a sensitivity-based joint unscented Kalman filter. To achieve robust estimation without parameter estimation drift during phases of insufficient excitation, a local sensitivity analysis is introduced. The friction estimation results are validated by utilizing measurements taken on a Volkswagen Golf GTE Plug-In Hybrid on four different road surfaces for longitudinal and lateral dynamic maneuvers.
UR - http://www.scopus.com/inward/record.url?scp=85052580166&partnerID=8YFLogxK
U2 - 10.23919/acc.2018.8431259
DO - 10.23919/acc.2018.8431259
M3 - Conference contribution
AN - SCOPUS:85052580166
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 2593
EP - 2598
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -