Self-adapting motion cueing algorithm based on a kinematics reference model

Due to a number of advantages over traditional development methods, the importance of
dynamic driving simulators in automotive research and development has grown continuously
in recent years. Motion simulation via motion cueing algorithms contributes significantly to the driving experience and provides the driver with valuable information about the current driving dynamics. The adaptation and tuning process of these algorithms can be difficult and timeconsuming tasks. It needs to be repeated after changes to the vehicle or driving scenario. This paper discusses and presents an adaptive or rather self-adapting motion cueing algorithm (MCA) concept. The approach is based on the integration of a kinematic reference model to dynamically and adaptively adjust the motion behavior dynamically and adaptively. This concept allows to reduce the parameter tuning effort drastically in long term, since the algorithm can adapt itself to different conditions such as vehicle type, driving situation, or driver behavior. In the following, the proposed algorithm structure is explained and illustrated. The advantages of the proposed MCA are demonstrated by an experimental comparison with a classical algorithm. Thereby it is shown how a self-adaptation of the algorithm can proceed and how to avoid violation of workspace boundaries.