Active Excitations for Maximum Friction Coefficient Estimation

verfasst von: Nicolas Lampe, Karl-Philipp Kortmann, Clemens Westerkamp, Hans-Georg Jacob
Abstract: For optimizing advanced driver assistance systems (ADAS) and implementing autonomous driving, knowledge of vehicle dynamics and the perception of the vehicle's environment is required. A crucial parameter influencing vehicle dynamics is the maximum friction coefficient between tires and road. Since this coefficient cannot be measured practically without high technical effort, model-based estimation algorithms are used. However, estimating the maximum friction coefficient is only possible with sufficient vehicle dynamic excitation, as this coefficient is then observable. Since maneuvers with sufficient excitation are rare during normal driving, in this paper, different levels of active excitations are used to enable observability and estimation of the maximum friction coefficient during maneuvers with insufficient vehicle dynamic excitation. First, a vehicle dynamic model is presented and analyzed regarding the observability during active excitations. Second, model-based estimation using an unscented Kalman filter (UKF) is implemented for the test vehicle and the UKF parameters are tuned for active excitations. Finally, model-based maximum friction coefficient estimation using onboard vehicle sensors is enabled by using active excitations. The experimental results show that is possible to estimate the maximum friction coefficient with a low error as well as a low credibility for maneuvers with insufficient vehicle dynamic excitation by using active excitations.
Organisationseinheit(en): Institut für Mechatronische Systeme
Externe Organisation(en): Hochschule Osnabrück
Typ: Aufsatz in Konferenzband
Publikationsdatum: 2023
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Angewandte Informatik, Fahrzeugbau, Modellierung und Simulation
Elektronische Version(en): https://doi.org/10.1109/IV55152.2023.10186603 (Zugang: Geschlossen)