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

TL;DR

This work tackles the problem of 3D vehicle dynamics state estimation for high-speed autonomous racing under varying road geometries and degraded signal quality. It fuses an EKF with a point-mass model and a UKF with an integrated STM, augmented by reference road angles and virtual velocity measurements, to estimate position, orientation, and velocities while inferring road incline and banking. The proposed ACOR strategy (adaptive covariance with Mahalanobis outlier rejection) ensures smooth, stable estimates during GNSS dropouts, and the 3D estimator is shown to outperform a planar 2D-EKF and an industry INS on LVMS and MON tracks. The results demonstrate online road-angle estimation and accurate side-slip prediction, highlighting the approach’s potential for robust control in autonomous racing and setting the stage for further enhancements such as LiDAR fusion and vehicle-specific parameter estimation.

Abstract

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.

Figures (8)

• Figure 1: Sensor Setup of the Dallara AV-21 race car used for data recording on the Las Vegas Motorspeedway.
• Figure 2: High-level architecture of the state estimation software stack highlighting the core components and their interaction.
• Figure 3: Banking angle at LVMS based on aerial LiDAR measurements and calculated reference angles.
• Figure 4: Covariance adaptation strategy after a signal dropout. ${\sigma_{GNSS}}$ symbolizes the reported standard deviation of the GNSS receiver, while ${\sigma_{adapt}}$ represents the standard deviation used for the adaptive covariance of the EKF.
• Figure 5: Longitudinal velocity profile at LVMS.