Front vehicle lateral motion state real-time estimation method considering random measurement time lag

Abstract

The invention discloses a front vehicle lateral motion state real-time estimation method considering random measurement time lag, and belongs to the technical field of vehicle control. The invention aims at carrying out modeling description on a vehicle lateral motion system considering measurement time lag, and then designing a front vehicle lateral motion state real-time estimation method considering random measurement time lag under a Moving Horizon Estimation (MHE) framework according to an established model. The method comprises the steps of building a high-fidelity vehicle model, modeling and describing vehicle lateral motion considering measurement time lag, and designing an estimator. According to the method, the model precision is improved, and better robustness is achieved for external interference.

Description

technical field [0001] The invention belongs to the technical field of vehicle control. Background technique [0002] With the continuous development of artificial intelligence technology and information technology, the intelligence and informatization of automobiles are also continuously improved, and various advanced driver assistance systems and automatic driving systems have also been developed and applied. For intelligent vehicles or advanced driver assistance systems, the vehicle's decision-making, planning and control systems need to obtain the necessary information support. Therefore, under this background, it is necessary to obtain the lateral motion state of the vehicle in front. But because the vehicle is a complex nonlinear system, some key motion information cannot be directly measured on the basis of the existing sensor technology. Therefore, estimation algorithms need to be designed to estimate information that cannot be directly measured based on limited me...

AI Technical Summary

Problems solved by technology

Linear simplification will introduce estimation error, even when the nonlinearity of the system is strong, it will cause the estimator to be unstable and the estimated value to diverge

[0005] 3. In the design of traditional estimators, such as Kalman filter (KF) and extended Kalman filter (EKF), only the measured value at the current moment is considered, which seriously limits the accuracy of the estimator

First of all, when the time lag is large, the measured value at the current moment cannot be obtained, and only the available measured value at the latest moment can be used, which will cause an increase in the estimation error

Secondly, if only the measured value at the current moment is considered, when the sensor is affected by a short-term disturbance, the estimation error will increase significantly, and the convergence speed will be slower after the disturbance disappears.

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