Comparison of Transfer Function Models to Represent the Correlation Between Vehicle Lateral Acceleration and Head Tilting Angle in Motion Sickness

Ali, Yassir; Saruchi, Sarah 'Atifah · 2022 · Crossref

DOI: 10.15282/ijame.19.4.2022.01.0775

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Summary

This study addresses the need for mathematical models to quantify the correlation between vehicle lateral acceleration and occupant head tilting behavior, a key factor in motion sickness (MS) severity. While MS is widely understood through sensory conflict theories, the specific relationship between vehicle dynamics and occupant posture remains indefinite. The authors aim to develop and compare transfer function models that map lateral acceleration (input) to head rolling angle (output) for both drivers and passengers, thereby providing a mathematical representation of this correlation to aid in MS mitigation strategies. The methodology utilized experimental data from a prior study involving ten healthy participants in a Proton Exora multi-purpose vehicle. Participants performed slalom driving maneuvers at a constant speed of 30 km/h along a 150-meter track with cones spaced 20 meters apart, generating lateral acceleration at a frequency of 0.21 Hz. Sensors measured lateral acceleration at the vehicle’s center of gravity and head rolling angles via caps worn by participants. Using MATLAB’s System Identification (SI) toolbox, the authors applied black-box modeling to estimate transfer functions of second, third, and fourth orders. Data was split into estimation and validation sets using proportions of 80-20%, 70-30%, and 60-40%. The models were validated using unseen data in Simulink, and their efficiency was assessed via fit percentages and Root Mean Square Error (RMSE). The results confirmed that drivers tilt their heads toward the centripetal direction (opposite to lateral acceleration), while passengers tilt toward the centrifugal direction, correlating with higher MS intensity for passengers. The highest model fit percentages were 67.87% for drivers and 67.93% for passengers, both achieved with fourth-order transfer functions using an 80-20% data split. Third-order models yielded fits of 66.78% (drivers) and 66.3% (passengers), while second-order models achieved 65.17% and 64.82%, respectively. Validation via Simulink demonstrated low RMSE values, with driver models ranging from 1.7351 to 1.8325 and passenger models from 2.5419 to 2.7349. These results surpassed previous studies in accuracy, confirming the acceptability of the derived transfer functions. The significance of this work lies in its provision of validated mathematical models that accurately represent the dynamic relationship between vehicle movement and occupant head posture. These models can inform the design of vehicle control systems aimed at reducing motion sickness by optimizing lateral acceleration profiles or suggesting compensatory head movements. The study concludes that while current linear transfer functions are effective, future research should explore non-linear modeling strategies and larger datasets to further enhance prediction accuracy and contribute to improved occupant comfort in automotive and autonomous vehicle applications.

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• passenger motion sickness comfort
• simulator sickness

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• Theoretical Contribution: computational model