Joint estimation of vehicle states and spatio-temporal tyre–road friction using sequential Monte Carlo
Abstract
Accurately estimating and predicting tyre–road friction is essential for optimising safety-critical control strategies and task planning in advanced driver assistance and autonomous driving systems. Recent approaches propose the integration of camera and vehicle dynamics-based estimation methods. These hybrid approaches achieve more reliable estimation but cannot retain and recall this information for a location. They are also unable to quantify uncertainty in tyre–road friction with respect to changing road conditions. This paper proposes a novel scheme for estimating tyre–road friction under unknown road conditions. A 2D map of the environment is combined with a vehicle dynamics model to enable joint estimation of environment and vehicle state, including tyre–road friction. A tailored sequential Monte Carlo scheme enables online updating of map information and allows for recall upon revisiting a location. Results show that uncertainty in tyre–road friction can be reduced during successive trials of emergency braking. As a result, braking distance predictions become more accurate, with an RMSE reduction of 4.95 %, averaged across three road types.
Details
- Organisationseinheit(en)
-
Institut für Mechatronische Systeme
- Externe Organisation(en)
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ZF Friedrichshafen AG
- Typ
- Artikel
- Journal
- Vehicle system dynamics
- ISSN
- 0042-3114
- Publikationsdatum
- 25.03.2026
- Publikationsstatus
- Elektronisch veröffentlicht (E-Pub)
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Fahrzeugbau, Sicherheit, Risiko, Zuverlässigkeit und Qualität, Maschinenbau
- Elektronische Version(en)
-
https://doi.org/10.1080/00423114.2026.2645983 (Zugang:
Offen
)