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Incorporating driver activity in holistic comfort models.

Peter Moertl1, Margit Hoefler1, Benjamin Ewerz1

  • 1Virtual Vehicle Research Center Inffeldgasse, Graz, Austria.

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|December 18, 2020
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Summary

A new holistic comfort model in vehicles distinguishes between physiological and emotional comfort, influenced by cognitive processes. Study findings support this model, showing distinct impacts of activities on different comfort dimensions.

Keywords:
Virtual developmentautomotive comfortcomfortcomfort modelholistic comforthuman-systems integration

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Area of Science:

  • Human-Computer Interaction
  • Automotive Engineering
  • Psychology

Background:

  • Existing vehicle comfort models are expanded by a new holistic comfort model.
  • This model proposes two dimensions: physiological comfort (thermal, auditory, postural) and emotional comfort (activity-based).
  • Both dimensions are moderated by cognitive processes, influencing the overall comfort experience.

Purpose of the Study:

  • To introduce and validate a holistic comfort model for vehicle occupants.
  • To test hypotheses derived from the proposed comfort model.
  • To investigate the impact of driver and passenger activities on comfort.

Main Methods:

  • A driving simulator study was conducted with 32 participants.
  • Participants assessed holistic and physiological comfort under varied driving conditions.
  • Principal component analysis was used to analyze comfort responses.

Main Results:

  • Participant responses aligned with two principal components, reflecting holistic and physiological comfort.
  • Driving and non-driving activities differentially impacted holistic versus physiological comfort.
  • Results support the proposed model's distinction between comfort dimensions.

Conclusions:

  • The holistic comfort model provides a framework for understanding vehicle occupant comfort.
  • Cognitive processes play a crucial role in moderating physiological and emotional comfort.
  • Activity types significantly influence comfort experiences, validating the model's predictions.