An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

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.

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

“…The detailed estimation algorithm for cornering stiffness is presented in [181]. One of the primary challenges faced by these estimators is their limited accuracy when dealing with high wheel slips on low-friction roads or sudden changes in friction coefficients, often referred to as "friction jumps" [189]. An effective estimator example is the one based on the Unscented Kalman Filter (UKF); while demanding more computational resources compared to the EKF, it offers the advantage of not requiring the linearisation of the model [190].…”