Implementation of Suitable Comfort Model for Posture and Motion Prediction in DHM Supported Vehicle Design

AbstractDriver-vehicle interaction analyses are done to ensure a successful vehicle design from an ergonomics perspective. Digital Human Modelling (DHM) tools are often used to support such verifications, particularly at early stages of the product development process. When verifying that a vehicle design accommodates the diversity of users and tasks, a DHM tool needs to be able to represent postures and motions that are likely under certain conditions. This functionality is essential so that the tool user will obtain objective and repeatable simulation results. The DHM tool IMMA (Intelligently Moving Manikins) predicts postures and motions by using computational methods. This offers the possibility to generate postures and motions that are unique for the present design conditions. IMMA was originally developed for simulating manual assembly work, whereas the work presented here is a step towards utilizing the IMMA tool for occupant packaging and related tasks. The objective is a tool for virtual verification of driver-vehicle interaction that supports and automates the simulation work to a high degree. The prediction functionality in IMMA is based on the use of optimization algorithms where one important component is the consideration of comfort level. This paper reports results from an basic investigation of driving postures and available comfort models suitable in a driving context, and shows initial results of seated posture and motion prediction functionality in the IMMA tool