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Human Factors: The Journal of the Human Factors and Ergonomics Society
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A Statistical Method for Predicting Automobile Driving Posture

Matthew P. Reed

University of Michigan Transportation Research Institute, Ann Arbor, Michigan

Miriam A. Manary

University of Michigan Transportation Research Institute, Ann Arbor, Michigan

Carol A. C. Flannagan

University of Michigan Transportation Research Institute, Ann Arbor, Michigan

Lawrence W. Schneider

University of Michigan Transportation Research Institute, Ann Arbor, Michigan

A new model for predicting automobile driving posture is presented. The model, based on data from a study of 68 men and women in 18 vehicle package and seat conditions, is designed for use in posturing the human figure models that are increasingly used for vehicle interior design. The model uses a series of independent regression models, coupled with data-guided inverse kinematics, to fit a whole-body linkage. An important characteristic of the new model is that it places greatest importance on prediction accuracy for the body locations that are most important for vehicle interior design: eye location and hip location. The model predictions were compared with the driving postures of 120 men and women in five vehicles. Errors in mean eye location predictions in the vehicles were typically less than 10 mm. Prediction errors were largely independent of anthropometric variables and vehicle layout. Although the average posture of a group of people can be predicted accurately, individuals' postures cannot be predicted precisely because of interindividual posture variance that is unrelated to key anthropometric variables. The posture prediction models developed in this research can be applied to posturing computer-rendered human models to improve the accuracy of ergonomic assessments of vehicle interiors.

Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 44, No. 4, 557-568 (2002)
DOI: 10.1518/0018720024496917
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