Joint estimation method for motion state of vehicle and gradient of road

Abstract

The invention discloses a joint estimation method for the motion state of a vehicle and the gradient of a road. The joint estimation method comprises the following steps of constructing a ramp drivingplatform; estimating the motion state of the vehicle by using a square-root adaptive variational Bayesian cubature Kalman filtering algorithm, and forming a vehicle motion state estimation module; building a road gradient unit, and solving a road gradient value based on a ridge regression algorithm to form a road gradient estimation module; and adopting interaction iteration between the two modules to realize joint estimation of the vehicle motion state and the road gradient. The square-root adaptive variational Bayesian cubature Kalman filtering algorithm can adapt to the square root of a state covariance and the square root of a measurement noise covariance, and has the characteristics of small calculation amount, good real-time performance and high precision when compared with a similar noise adaptive algorithm. Based on the joint estimation for the vehicle motion state and the road gradient, the relationship between the vehicle motion state and the road gradient is considered, andthe accuracy of estimation results can be improved.

Description

technical field [0001] The invention belongs to the field of vehicle motion state estimation, in particular to a joint estimation method for vehicle motion state and road slope. Background technique [0002] Since the 20th century, the number of cars in the world has continued to increase, and at the same time, the number of traffic accidents related to cars has also increased year by year. The active safety system installed on the car can improve the driving stability of the car and reduce the occurrence of traffic accidents. The active safety system of automobiles makes decision-making control by collecting vehicle motion status information and environmental information to improve the safety of automobile driving. It can be said that the real-time and accurate estimation of vehicle motion state information and environmental information is the key factor for the timely response of the automotive active safety system. [0003] At present, some achievements have been made i...

AI Technical Summary

Problems solved by technology

Changes in road gradient reduce vehicle driving stability, resulting in increased variation in process noise and measurement noise when estimating the state of motion of the vehicle

In order to solve this problem, the prior art proposes a variational Bayesian adaptive volumetric Kalman filter algorithm, but this algorithm needs to complete several iterative adaptive calculations every time it estimates the state, which greatly increases the algorithm The amount of calculations leads to poor real-time performance and cannot meet actual needs

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