Sequential Motion Cueing Algorithms: Using the Best Algorithm in each Situation

Motion Cueing Algorithms (MCAs) aim to create realistic motion experiences in driving simulators. To this end, a range of MCAs exists with each algorithm having its own advantages and disadvantages. Hence, different MCAs perform best in different situations. In this work, we propose methodology for sequentially switching between MCAs at simulation runtime with the goal of allowing the best motion cueing in each situation. To cope with the challenge of switching, a model predictive control (MPC) formulation is suggested, which controls the simulator and vehicle to the required states of the next MCA. Criteria on when to switch MCAs are defined. By deriving general formulations, switching between any of the existing MCAs is possible. A representative switching scenario between two different MCA types is analyzed, showing the effectiveness of the proposed methodology. The achieved switching potentially increases realism, while promising more flexibility in simulator studies.     «

Motion Cueing Algorithms (MCAs) aim to create realistic motion experiences in driving simulators. To this end, a range of MCAs exists with each algorithm having its own advantages and disadvantages. Hence, different MCAs perform best in different situations. In this work, we propose methodology for sequentially switching between MCAs at simulation runtime with the goal of allowing the best motion cueing in each situation. To cope with the challenge of switching, a model predictive control (MPC)...     »