Abstract:

In order to estimate the vehicle yaw rate and increase the estimator accuracy, a method of BP(Back Propagation) neural network is adopted to estimate the vehicle yaw rate during the steering condition. There are four kinds of roads that are existing in reality: dry road, pitch road, watered road and ice road, and one neural
network cannot include the condition of four kinds of different roads. To solve the problem and increase the precision of the network estimator, we have trained four neural networks respectively to form a network group. By adding a selecting module to the system, the estimation value of yaw rate with corresponding road friction
coefficient can be picked out. We obtain the residuals of networks by co-simulation of AMESim and MATLAB.Finally, we evaluate and analyse the results of the estimation generating by the estimator. The method adopted is the data-based approach. Compared with the existing model-based method, it is independent of precise model and it can estimate the yaw rate precisely. Simulation results and analysis verified the viability and the precision of using BP neural networks to estimate vehicle yaw rate.

Key words: yaw rate estimation, back propagation(BP) neural network, friction coefficient, co-simulation of AMESim and Matlab