# Three-Dimensional Vehicle Dynamics State Estimation for High-Speed Race Cars under varying Signal Quality

arXiv: https://arxiv.org/abs/2408.14885 · HTML: https://mlsanity.com/paper/2408.14885/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow](https://mlsanity.com/paper/2505.11116/) (2025)
- [Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors](https://mlsanity.com/paper/2607.05663/) (2026)
- [Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba](https://mlsanity.com/paper/2607.05669/) (2026)

## Abstract (arXiv)

This work aims to present a three-dimensional vehicle dynamics state estimation under varying signal quality. Few researchers have investigated the impact of three-dimensional road geometries on the state estimation and, thus, neglect road inclination and banking. Especially considering high velocities and accelerations, the literature does not address these effects. Therefore, we compare two- and three-dimensional state estimation schemes to outline the impact of road geometries. We use an Extended Kalman Filter with a point-mass motion model and extend it by an additional formulation of reference angles. Furthermore, virtual velocity measurements significantly improve the estimation of road angles and the vehicle's side slip angle. We highlight the importance of steady estimations for vehicle motion control algorithms and demonstrate the challenges of degraded signal quality and Global Navigation Satellite System dropouts. The proposed adaptive covariance facilitates a smooth estimation and enables stable controller behavior. The developed state estimation has been deployed on a high-speed autonomous race car at various racetracks. Our findings indicate that our approach outperforms state-of-the-art vehicle dynamics state estimators and an industry-grade Inertial Navigation System. Further studies are needed to investigate the performance under varying track conditions and on other vehicle types.