Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially-available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state-of-the-art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics-, dynamics- and neural network-based estimators. A hardware-in-loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics-based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics-based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network-based estimator can perform well in all manoeuvres once the training datasets are properly selected.

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