A multi-level estimation of road condition using an adaptive observer combined with periodical σ-modification

A method that is designed to distinguish the road condition from several prespecified candidates is proposed. Although the method allows a systematic design procedure on the basis of a vehicle dynamical model, the method is not intended to provide an exact estimation, but to improve robustness against modeling error. A road condition is characterized by two parameters, and they are estimated by a gradient-based adaptive law combined with periodical σ-modification, which plays an essential role in multilevel estimation and robustness against modeling error. Field test data of four different road conditions are applied to the proposed algorithm, and it successfully distinguishes the road condition only by prevalent sensors