A Model-Based Online Reference Prediction Strategy for Model Predictive Motion Cueing Algorithms

Abstract

Interactive driving simulation has become a key technology to support the development and optimization process of modern vehicle components and driver assistance systems both in academic research and in the automotive industry. However, the validity of the results obtained within the virtual environment depends essentially on the adequate reproduction of the simulated vehicle movements and the corresponding immersion of the driver. For that reason, specific motion platform control strategies, so-called Motion Cueing Algorithms (MCA), are used to replicate the simulated accelerations and angular velocities within the physical limitations of the driving simulator best possible. In this paper, we present a novel model-based approach to predict oncoming vehicle motion at runtime. For that purpose, a virtual driver model as well as a simplified vehicle dynamics model are introduced to estimate the future driver inputs and the resulting vehicle trajectories according to the current driving situation. This additional system knowledge enables control algorithms designed on the idea of Model Predictive Control (MPC) to exploit their potential more efficiently. The performance of the proposed prediction strategy is evaluated on the basis of measurement data from a real test run in comparison to an ideal prediction and a constant reference, using a hybrid kinematics motion system as an application example.