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Neural Network based Tire-Road Friction Estimation Using Experimental Data

  • Knowledge of the maximum friction coefficient µmax between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µmax 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 µmax 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.

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Metadaten
Author:Nicolas Lampe, Karl-Philipp Kortmann, Clemens WesterkampORCiD
Title (English):Neural Network based Tire-Road Friction Estimation Using Experimental Data
URL:https://www.sciencedirect.com/science/article/pii/S2405896323023893?via%3Dihub
DOI:https://doi.org/10.1016/j.ifacol.2023.12.056
Parent Title (German):IFAC PapersOnLine
Publisher:Elsevier
Document Type:Conference Proceeding
Language:English
Year of Completion:2023
electronic ID:Zur Anzeige in scinos
Release Date:2024/04/03
First Page:397
Last Page:402
Note:
3rd Modeling, Estimation and Control Conference MECC 2023: Lake Tahoe, USA, October 2-5, 2023

Open Access
Faculties:Fakultät IuI
DDC classes:600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau
Review Status:Veröffentlichte Fassung/Verlagsversion
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International