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.
| Author: | Nicolas Lampe, Karl-Philipp Kortmann, Clemens Westerkamp |
|---|---|
| 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): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |