Neural Network based Tire-Road Friction Estimation Using Experimental Data

verfasst von

Nicolas Lampe, Karl-Philipp Kortmann, Clemens Westerkamp

Abstract

Knowledge of the maximum friction coefficient µ

_max between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µ

_max estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µ

_max estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Identifikation & Regelung

Externe Organisation(en)

Hochschule Osnabrück

Typ

Konferenzaufsatz in Fachzeitschrift

Journal

IFAC-PapersOnLine

Band

Seiten

397-402

Anzahl der Seiten

ISSN

2405-8963

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2023.12.056 (Zugang: Offen)