Vehicle Motion State Estimation Based on WOA-SVR

In order to accurately obtain vehicle motion state information without relying on the accuracy 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 designed 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 WOA 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. © 2024 Chinese Mechanical Engineering Society. All rights reserved.