Model-Based Maximum Friction Coefficient Estimation for Road Surfaces with Gradient or Cross-Slope

verfasst von

Nicolas Lampe, Simon Friedrich Gerhard Ehlers, Karl-Philipp Kortmann, Clemens Westerkamp, Thomas Seel

Abstract

For the development of advanced driver assistance systems (ADAS) and autonomous driving, a perception of the vehicle's environment is necessary. This includes, among others, road gradients, cross-slopes, and the road surface condition, with the maximum friction coefficient of the tire-road contact as a safety-relevant parameter. However, these three road parameters cannot be measured directly while driving by sensors installed in modern vehicles. Current estimation methods provide either the maximum friction coefficient or the road gradient and cross-slope but never combined. Since the road angles influence the maximum friction coefficient estimation and vice versa, separate estimation of these parameters, in general, leads to incorrect estimation results. In this paper, a new Unscented Kalman Filter (UKF)-based approach is proposed for simultaneous estimation of all three mentioned road parameters. For this purpose, a dynamic vehicle model considering road gradients and cross-slopes is introduced and integrated into the UKF. It is demonstrated that, in contrast to a state-of-the-art UKF, the proposed algorithm yields improved accuracy and correct maximum friction coefficient estimates even on roads with gradients or cross-slopes.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Externe Organisation(en)

Hochschule Osnabrück

Typ

Aufsatz in Konferenzband

Seiten

2141-2147

Anzahl der Seiten

7

Publikationsdatum

02.06.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Angewandte Informatik, Fahrzeugbau, Modellierung und Simulation

Elektronische Version(en)

https://doi.org/10.1109/IV55156.2024.10588642 (Zugang: Geschlossen)