Statistical Models for Predicting Automobile Driving Postures for Men and Women Including Effects of Age

Abstract

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.