Real-time Estimation Method of Front Vehicle's Lateral Motion State Considering Random Measurement Time Delay

Abstract

The invention relates to a real-time estimation method of the lateral motion state of a preceding vehicle considering random measurement time lag, which belongs to the technical field of vehicle control. The object of the present invention is to carry out modeling description to the vehicle lateral motion system considering measurement time lag, then according to the established model, design the front vehicle lateral motion state real-time under the framework of rolling time domain estimation (Moving Horizon Estimation-MHE) Estimation method for real-time estimation of the lateral motion state of the preceding vehicle considering stochastic measurement time-lags. The steps of the invention are: building a high-fidelity vehicle model, modeling and describing the lateral movement of the vehicle considering the measurement time lag, and designing an estimator. The invention improves the accuracy of the model and has better robustness to external disturbances.

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.

Images