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Accommodation Assessments for Vehicle Occupants Using Augmented Reality

  • Byoung-keon Daniel Park
  • Matthew P. Reed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 822)

Abstract

This paper presents a new accommodation assessment method for vehicle occupants using a statistical body shape model in an augmented reality (AR) environment. Vehicle occupant accommodation assessment is an important aspect of vehicle interior design. Variability in body dimensions of the target population is a key component in determining the overall user accommodation. Statistical body shape modeling enables quantitative representation and assessment a wide range of variability in anthropometry and posture. These statistical models provide a way to efficiently generate a realistic 3d body shape surface along with the standard body dimensions, anatomical landmark locations and joint locations. In the current study, an automotive posture body shape model based on data from 255 men and women ages 20 to 95 years old was used in a demonstration of AR technology. Typically, quantitative assessment of a physical vehicle requires time-consuming scanning to obtain a computer model that can be used with virtual assessment tools. We addressed this issue by using AR to enable assessment without explicit model building. Apple ARKit on an iPhone was employed in this study to implement the model in an augmented vehicle environment. The system allows the user to place a human model in a vehicle by detecting the seat surfaces. The user is able to manipulate the body shape to assess accommodation across the range of anthropometric variability. Interior accommodation was assessed by measuring the distances between the certain points from both the model and the augmented physical environment, in addition to a qualitative visual inspection. Opportunities and impacts of the proposed AR approach with digital human models in more applications are discussed.

Keywords

Augmented reality Accommodation Seat Digital human models 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MichiganAnn ArborUSA

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