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The ease of getting into and out of passenger cars and light trucks is a critical component of customer acceptance and product differentiation. In commercial vehicles, the health and safety of drivers is affected by the design of the steps and handholds they use to get into and out of the cab. Ingress/egress assessment appears to represent a substantial application opportunity for digital human models. The complexity of the design space and the range of possible biomechanical and subjective measures of interest mean that developing useful empirical models is difficult, requiring large-scale subject testing with physical mockups. Yet, ingress and egress motions are complex and strongly affected by the geometric constraints and driver attributes, posing substantial challenges in creating meaningful simulations using figure models.

Standing reach envelopes are important tools for the design of industrial and vehicle environments. Previous work in this area has focussed on manikin-based (where a few manikins are used to simulate individuals reaching within the region of interest) and population-based (where data are gathered on many individuals reaching in a constrained environment) approaches. Each of these methods has merits and shortfalls. The current work bridges the manikin- and population-based approaches to assessing reach by creating population models using kinematic simulation techniques driven by anthropometric data. The approach takes into account body dimensions, balance, and postural cost to create continuous models that can be used to assess designs with respect to both maximal and submaximal reaches. Cost is quantified as the degree to which the torso is involved in the reach, since the inclination of the torso is a good measure of lower-back load and may be related to subjective reach difficulty.

A new method is presented for physically measuring drivers' field of view in rearview mirrors. A portable coordinate measurement apparatus (FARO Arm) is used to measure the mirror locations, contours, and curvature. Measurements of the driver's head and eye locations while looking into each mirror are also made. Raytracing is used to map the two- or three-dimensional field of view in each mirror. The method differentiates between monocular, binocular, and ambinocular fields of view, and can account for head movements. This method has been applied to passenger cars, light trucks, and heavy trucks to document how drivers aim their mirrors during normal use.

Recent research in the ASPECT (Automotive Seat and Package Evaluation and Comparison Tools) program has led to the development of a new method for automobile driver posture prediction, known as the Cascade Model. The Cascade Model uses a sequential series of regression functions and inverse kinematics to predict automobile occupant posture. This paper presents an alternative method for driver posture prediction using data-guided kinematic optimization. The within-subject conditional distributions of joint angles are used to infer the internal cost functions that guide tradeoffs between joints in adapting to different vehicle configurations. The predictions from the two models are compared to in-vehicle driving postures.

A new method of predicting automobile occupant posture is presented. The Cascade Prediction Model approach combines multiple independent predictions of key postural degrees of freedom with inverse kinematics guided by data-based heuristics. The new model, based on posture data collected in laboratory mockups and validated using data from actual vehicles, produces accurate posture predictions for a wide range of passenger car interior geometries. Inputs to the model include vehicle package dimensions, seat characteristics, and occupant anthropometry. The Cascade Prediction Model was developed to provide accurate posture prediction for use with any human CAD model, and is applicable to many vehicle design and safety assessment applications.

Many vehicle design and safety assessment applications use physical and virtual representations of vehicle occupants within the vehicle interior. Proper use of these human models requires accurate data concerning vehicle occupant posture and position. This paper presents techniques for characterizing vehicle occupant posture by measuring accessible body landmarks. The landmark locations are used to estimate joint locations that define a kinematic linkage representation of the human body. The resulting posture analysis techniques provide a unified method of measuring and reporting vehicle occupant postures that is suitable for use with both physical and virtual human models.