An Improved Seating Accommodation Model for Older and Younger Drivers

2016-01-1444

04/05/2016

Authors
Abstract
Content
The research objective was to measure and understand the preferred seat position of older drivers and younger drivers within their personal vehicles to influence recommended practices and meet the increased safety needs of all drivers. Improper selection of driver’s seat position may impact safety during a crash event and affect one’s capacity to see the roadway and reach the vehicle’s controls, such as steering wheel, accelerator, brake, clutch, and gear selector lever. Because of the stature changes associated with ageing and the fact that stature is normally distributed for both males and females, it was hypothesized that the SAE J4004 linear regression would be improved with the inclusion of gender and age terms that would provide a more accurate model to predict the seat track position of older drivers. Participants included 97 older drivers over the age of 60 and 20 younger drivers between the ages of 30 to 39. Data were collected within the driver’s personal vehicle to obtain natural, driver selected seat position. A hierarchical, multivariable, linear regression technique was used to improve upon the current SAE J4004 driver selected seat position equation. Results show that the addition of a gender term to the SAE J4004 recommended model for predicting driver selected seat position of any driver provided an insignificant contribution to the model; whereas, the addition of an age term to the same SAE J4004 is a statistically significant contribution to the model, thereby yielding a 2.5% increase in the fit of the model and accuracy of the predicted seat position.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-1444
Pages
13
Citation
McConomy, S., Brooks, J., Venhovens, P., Xi, Y. et al., "An Improved Seating Accommodation Model for Older and Younger Drivers," SAE Technical Paper 2016-01-1444, 2016, https://doi.org/10.4271/2016-01-1444.
Additional Details
Publisher
Published
Apr 5, 2016
Product Code
2016-01-1444
Content Type
Technical Paper
Language
English