Friction and Road Condition Estimation using Bayesian Networks

verfasst von

Björn Volkmann, Karl-Philipp Kortmann, Thomas Seel

Abstract

Knowledge about the maximum tire-road friction potential is an important factor to ensure the driving stability and traffic safety of the vehicle. Many authors proposed systems that either measure friction related parameters or estimate the friction coefficient directly via a mathematical model. However these systems can be negatively impacted by environmental factors or require a sufficient level of excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this work investigates, if a more robust estimation can be achieved by fusing the information of multiple systems using a Bayesian network, which models the statistical relationship between the sensors and the maximum friction coefficient. First, the Bayesian network is evaluated over its entire domain to compare the inference process to all possible road conditions. After that, the algorithm is applied to data from a test vehicle to demonstrate the performance under real conditions.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Identifikation & Regelung

Typ

Aufsatz in Konferenzband

Band

56

Seiten

854-861

Anzahl der Seiten

8

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2023.10.1672 (Zugang: Offen)