ArticleSensors (Basel, Switzerland)2024
An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Vehicle Sideslip Angle Estimation Using Deep Reinforcement Learning Combined with Unscented Kalman Filter.Sensors (Basel, Switzerland) · 2025Article
- Integrating GAN-based machine learning with nonlinear Kalman filtering for enhanced state estimation.Scientific reports · 2025Article
- Determining Tyre Adhesion Characteristics Based on the Road Tests of Automobiles.Sensors (Basel, Switzerland) · 2024Article
- Review of Integrated Chassis Control Techniques for Automated Ground Vehicles.Sensors (Basel, Switzerland) · 2024Review
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Authors and funding
9 authors.
Funding
Abstract
This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre-road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre-road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.
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