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Statistical models for predicting automobile driving postures for men and women including effects of age

In: Human Factors, 58(2):261-278. 2016.

Authors: Jangwoon Park, Matthew P. Reed, Jason J. Hallman

Background: Previously published statistical models of driving posture have been effective for vehicle design but have not taken into account the effects of age.

Objective: The present study developed new statistical models for predicting driving posture.

Methods: Driving postures of 90 U.S. drivers with a wide range of age and body size were measured in laboratory mockup in nine package conditions. Posture-prediction models for female and male drivers were separately developed by employing a stepwise regression technique using age, body dimensions, vehicle package conditions, and two-way interactions, among other variables.

Results: Driving posture was significantly associated with age, and the effects of other variables depended on age. A set of posture-prediction models is presented for women and men. The results are compared with a previously developed model.

Conclusion: The present study is the first study of driver posture to include a large cohort of older drivers and the first to report a significant effect of age.

Application: The posture-prediction models can be used to position computational human models or crash-test dummies for vehicle design and assessment.

Research Group: