In order to accurately obtain vehicle motion state information without relying on the ac-curacy of the dynamics model,a vehicle state estimation algorithm was proposed based on WOA-SVR.Firstly,by analyzing the basic characteristics of vehicle dynamics,a SVR architecture was de-signed for estimating the separation of lateral velocity,yaw rate,and vehicle speed.Then,the SVR model was trained on a dataset composed of multiple driving conditions,and the WO A was used to optimize the penalty factor c and kernel function parameter g in the relaxation variables during the training processes.Finally,the estimation algorithm was validated through virtual simulation of single line shift and frequency sweep tests,as well as ABS braking and double line shift actual vehicle tests.The results show that this algorithm effectively improves estimation accuracy and is robust to changes in speed,enabling accurate estimation of vehicle motion states without relying on dynamics models.
vehicle state estimationdynamics modelmachine learningsupport vector regres-sion(SVR)whale optimization algorithm(WOA)
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