Abstract
Vehicle active safety control was a key technology to avoid serious safety accidents, and accurate acquisition of vehicle states signals was a necessary prerequisite to achieve active vehicle safety control. Based on the purpose, a 3-DOF nonlinear vehicle dynamics model containing constant noise and a nonlinear tire model were established, and several vehicle key states were estimated by a strong tracking central different Kalman filter (CDKF). The conclusion showed that the proposed estimator had higher accuracy and less computation requirement than the CKF, CDKF, and UKF estimators. Numerical simulation and experiments indicated that the proposed vehicle state estimation method not only had higher estimation accuracy but also had higher real-time function.
Highlights
Accurate and reliable state estimation is one of the necessary factors for vehicle active safety control
As the standard test methods for vehicle stability control, the double lane change road and the slalom road operating conditions have good objectivity and repeatability. e purpose of vehicle state parameter estimation in this paper is to provide a basis for subsequent vehicle stability control
A 3-DOF vehicle state estimation model and a modified Fiala model are established for the problem of vehicle state estimation
Summary
Accurate and reliable state estimation is one of the necessary factors for vehicle active safety control. Kim et al [10] proposed an interacting multiple model (IMM) approach using extended Kalman filters (EKFs) to improve multitarget state estimation performance with utilization of automotive radars. Hamze and Ali [2] proposed the extended Kalman filter approaches in order to measure the state variables directly in an articulated heavy vehicle. Liu et al [21] presented a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network to solve the problem that it was hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement was high.
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