Finite-memory estimation for vehicle roll and road bank angles

This paper proposes a novel finite-memory (FM) estimation method for vehicle roll and road bank angles. We consider a three-degree-of-freedom model based on bicycle and vehicle roll models, where there exist uncertainties in the model parameters. The existing estimation methods may show poor performance for the considered model with uncertainties. The accumulated errors, which might be caused by modeling uncertainties and numerical errors, may degrade the performance of existing methods because they use all past measurements. To overcome the weaknesses of the existing methods, a new FM estimation method is proposed and can prevent accumulated errors by using recent finite measurements. Both the simulation and experimental results show that the proposed estimation method ensures more reliable and accurate performance than the existing methods under various driving conditions.