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Assessing the Autonomic and Behavioral Effects of Passive Motion in Rats using Elevator Vertical Motion and Ferris-Wheel Rotation
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A Visual-Vestibular Model to Predict Motion Sickness for Linear and Angular Motion.

Daniel Sousa Schulman1, Nishant Jalgaonkar1, Sneha Ojha1

  • 1University of Michigan, Ann Arbor, MI, USA.

Human Factors
|September 12, 2023
PubMed
Summary
This summary is machine-generated.

A new model predicts passenger motion sickness by analyzing visual-vestibular conflicts in realistic driving scenarios. This tool aids in designing interventions and improving autonomous vehicle passenger experiences.

Keywords:
autonomous drivingmotion sicknessmultisensory integrationperception-actionsimulationusability/acceptance measurement and research

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Area of Science:

  • Human-computer interaction
  • Automotive engineering
  • Neuroscience

Background:

  • Existing motion sickness models lack accuracy in realistic driving conditions.
  • A need exists for models incorporating 3D visual-vestibular motion inputs.
  • Passenger motion sickness prediction requires improved models.

Purpose of the Study:

  • To propose and validate a novel model for predicting passenger motion sickness.
  • To account for visual-vestibular conflict in motion sickness prediction.
  • To enhance the understanding of factors contributing to passenger discomfort.

Main Methods:

  • Integrated subjective vertical conflict theory and human motion perception models.
  • Developed a novel architecture integrating visual and vestibular 6 Degrees of Freedom (DoF) motion signals.
  • Validated the model against experimental data from motion simulators and on-road testing.

Main Results:

  • Model predictions showed congruence with observed motion sickness trends in both simulated and real-world driving.
  • Successfully predicted motion sickness variations based on passenger tasks (e.g., handheld device use vs. forward-facing).
  • Demonstrated the model's capability in realistic driving conditions.

Conclusions:

  • The model effectively predicts motion sickness trends under varying visual-vestibular conflict.
  • Further validation with larger populations is recommended for comprehensive performance assessment.
  • The model serves as a valuable tool for mitigating passenger motion sickness and informing autonomous vehicle design.