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Personalizing motion sickness models: estimation and statistical modeling of individual-specific parameters.

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  • 1Faculty of Mechanical Engineering, Cognitive Robotics, Delft University of Technology, Delft, Netherlands.

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Individualized motion sickness models improve prediction accuracy in automated vehicles. This personalized approach captures unique user susceptibility, enhancing safety and comfort during non-driving activities.

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automated vehiclesdriving simulatorsmodelingmotion sicknesssimulator sickness

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Area of Science:

  • Human-computer interaction
  • Automotive engineering
  • Human factors psychology

Background:

  • Automated vehicles shift users from drivers to passengers, leading to non-driving activities and potential motion sickness.
  • Discrepancies between expected and perceived motion in vehicles and simulators cause sickness, necessitating improved motion control.
  • Individual differences in motion sickness susceptibility require personalized countermeasures.

Purpose of the Study:

  • To develop and validate a personalized framework for predicting motion sickness in automated vehicles.
  • To capture individual differences in motion sickness susceptibility across various motion and visual conditions.
  • To enhance the accuracy of motion sickness prediction models for personalized interventions.

Main Methods:

  • Combined a group-averaged sensory conflict model with an individualized Accumulation Model (AM).
  • Validated the framework using three datasets from vehicle and simulator experiments under passive motion conditions.
  • Utilized an individualized AM (AM2) with two parameters (gain K1 and time constant T1) to model individual responses.

Main Results:

  • The AM2 model achieved an average improvement factor of 1.7 in fitting individual motion sickness responses compared to the group-averaged AM0 model.
  • The AM2 model accurately modeled individual sickness responses across diverse motion and vision conditions using personalized parameters.
  • A Gaussian mixture model of parameter distributions predicted motion sickness in an unseen dataset with an average RMSE of 0.47.

Conclusions:

  • The proposed individualized Accumulation Model (AM2) framework effectively captures personal motion sickness susceptibility in automated vehicles.
  • This personalized approach enhances prediction accuracy and reduces the need for extensive population-level testing.
  • The framework offers a robust solution for developing tailored motion control strategies to mitigate sickness in automated vehicle occupants.