Estimation of the Steering Angle Based on Extended Kalman-Filter

Abstract

Many vehicle state parameters such as the sideslip angle, yaw rate, and steering angle are important for the Advanced Driver Assist System and vehicle safety system. In the past, most methods used to estimate the vehicle state parameters were based on models with directly measured parameters (steering angle, yaw rate, etc.). In this paper, we pro-pose a method to estimate the vehicle state parameters (sideslip angle, yaw rate, and steering angle) based on the Extended Kalman Filter (EKF). The EKF is designed to deal with the bicycle model, linear tire model, and steering wheel model with measurements from in-vehicle sensors such as the electronic stability control system. Therefore, the re-sults show that the proposed algorithm for estimating the vehicle state parameters, side-slip angle, yaw rate, and steering angle can effectively estimate the vehicle state parame-ters when the speed of the vehicle varies. The results from this study can be evaluated and analyzed by evaluating the root mean square error. In future, the proposed algorithm can be used not only for the design of an automatic control system for the tracking vehicle but also for steering system fault diagnosis.