On accurate estimation of vehicle lateral states based on an improved adaptive unscented Kalman filter

Abstract

This paper proposes an improved adaptive unscented Kalman filter (iAUKF)-based vehicle lateral state estimation method. A three-degree-of-freedom vehicle dynamics model is first established. Second, the influence of process noise and measurement noise on vehicle lateral state estimation using standard UKF is analyzed, and a new type of normalized innovation square-based adaptive noise covariance adjustment strategy is designed and incorporated into the standard UKF to form the iAUKF algorithm with the purpose of achieving accurate estimation of vehicle lateral states. Finally, a comparative simulation investigation using CarSim and MATLAB/Simulink is conducted to validate the effectiveness of the proposed method, and the results show that the proposed iAUKF-based estimation method has higher accuracy and stronger robustness against the standard UKF algorithm.

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

“…However, the effectiveness of the resulting filter is limited to specific conditions, and significant issues can occur during real-world operation. An effective approach to improve predictability and handle situations where the uncertainty characteristics of the system change over time is to employ adaptive covariance matrices [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. The adaptation approaches can either be based on theoretical algorithms, e.g., using fading factors to give higher weight to the last filter iterations, or on covariance definition rules, e.g., imposed through fuzzy logic algorithms or other heuristics benefitting from the understanding of vehicle dynamics.…”

“…Although this algorithm can offer stability in scenarios with noisy or incomplete data, its complexity could become critical when applied to filters characterised by vehicle models with higher number of degrees of freedom (DoFs). Reference [ 42 ] discusses a normalized innovation squared (NIS) algorithm to adaptively change both process and measurement noise covariances, by comparing the difference between predicted and observed measurements to the predicted covariance of measurement noise. However, alongside its computational complexity, the NIS approach requires precise tuning, and lacks robustness when encountering model inaccuracies.…”

“…The proposed method has some improvement effect compared with the original method, but the effect is not significant enough in some conditions, which is a defect of the proposed method”. In summary, the adaptation solutions in [ 41 , 42 , 43 ] primarily relied on the measurement noise level, and were not particularly effective for swift variations of states or tyre–road friction parameters, or for incorporating physics-based heuristics. Additionally, the proposed filters used simplified models, neglecting the wheel dynamics, which are of the essence for tyre slip dynamics estimation in extreme conditions.…”

Target Tracking Algorithm Based on Adaptive Strong Tracking Extended Kalman Filter

“…Ye [22] proposed a novel Adaptive Robust Cubature Kalman Filter (ARCKF) based on the H-infinity volume Kalman Filter (HCKF). Pang [23] introduced an adaptive noise covariance correction mechanism based on normalized innovation squares, which allowed for the precise lateral state estimate of vehicles. Likewise, Chen [24] integrated analytical gain matrices and adaptive factors into the classical desensitized ensemble Kalman filter to estimate the state.…”