Development of an Automobile Driving Posture Algorithm for Digital Human Models

Digital human models have greatly enhanced design for the automotive driving environment. The major advantage of the models today is their ability to quickly test a broad range of the population within specific design parameters. The need to create expensive prototypes and run time consuming clinics can be significantly reduced. However, while the anthropometric databases within these models are comprehensive, the ability to position the manikins in a driving posture is limited. This study collected driving postures for occupants in two vehicle packages, a passenger car and utility-type vehicle. In all instances the occupant was instructed to adjust the vehicle parameters so they were in their most comfortable position. The posture of the occupants was then compared to postural output from RAMSIS and Catia V5 HumanBuilder. The relationship between the actual and predicted postures will be used to create a comfortable driving posture algorithm based on vehicle package for the human models.