Comparison of Motion Cueing Algorithms based on Numerical Assessment of Motion Fidelity

Motion cueing algorithms (MACs) are essential in driving simulation systems to generate realistic motion feedback based on the driver interaction or the simulated environment. These algorithms aim to replicate the sensation of motion that corresponds to the visual and auditory cues experienced by a driver in a real-world scenario. Different algorithms, such as washout filters, adaptive and optimal algorithms, or predictive models, may be incorporated to enhance the fidelity of the motion cues. Motion fidelity assessment in a driving simulator involves evaluating the accuracy and realism of the motion cues provided to the driver. It ensures that the simulated motion aligns with the visual and auditory cues, creating a convincing and immersive driving experience. A comprehensive numerical assessment of motion fidelity in driving simulators involves a combination of metrics related to amplitude, timing, spectral characteristics, and driver experience. In the article, the comparison of the response of the MCAs applied into the motion platform based on KUKA robocoaster according to numerical criteria of motion fidelity (MF). The MCAs are first tuned parameters according to various levels of MF criteria and then the MCAs response are evaluated with another numerical method related to the cross-correlation analysis.