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SAE Recommended Practice J941 describes the eyellipse, a statistical representation of driver eye locations, that is used to facilitate design decisions regarding vehicle interiors, including the display locations, mirror placement, and headspace requirements. Eye-position data collected recently at University of Michigan Transportation Research Institute (UMTRI) suggest that the SAE J941 practice could be improved. SAE J941 currently uses the SgRP location, seat-track travel (L23), and design seatback angle (L40) as inputs to the eyellipse model. However, UMTRI data show that the characteristics of empirical eyellipses can be predicted more accurately using seat height, steering-wheel position, and seat-track rise. A series of UMTRI studies collected eye-location data from groups of 50 to 120 drivers with statures spanning over 97 percent of the U.S. population. Data were collected in thirty-three vehicles that represent a wide range of vehicle geometry.

A new approach to driver seat-position modeling is presented. The equations of the Seating Accommodation Model (SAM) separately predict parameters of the distributions of male and female fore/aft seat position in a given vehicle. These distributions are used together to predict specific percentiles of the combined male-and-female seat-position distribution. The effects of vehicle parameters-seat height, steering-wheel-to-accelerator pedal distance, seat-cushion angle, and transmission type-are reflected in the prediction of mean seat position. The mean and standard deviation of driver population stature are included in the prediction for the mean and standard deviation of the seat-position distribution, respectively. SAM represents a new, more flexible approach to predicting fore/aft seat-position distributions for any driver population in passenger vehicles. Model performance is good, even at percentiles in the tails of the distribution.

This study was initiated to evaluate the performance of the SAE J826 3-D manikin in seats that span a range of cushion firmness and contour levels. The manikin measures of H-point location, seatback angle, and seatpan angle (measured using a modified-manikin procedure) are compared with the human measures of hip-joint-center (HJC) location, torso angle, and thigh angle for forty drivers. The results indicate that the manikin H-point provides a reasonably consistent, though somewhat offset, measure of driver HJC location for the range of seats tested. This study found that seats with the same manikin-measured seatback angle produce different occupant torso angles. The data also suggest that for a given vehicle seat, the manikin-measured seatback angle can be used to predict the change in torso angle produced by adjusting the seatback inclination.

Dynamic seat-position testing conducted recently at UMTRI on several different vehicles indicates that, in many cases, the current seating accommodation model represented in SAE J1517 does not accurately predict the distribution of driver seat positions. In general, J1517 tends to predict population percentile seat positions that are forward of observed percentile seat positions, and differences can be as much as 60 mm. It was hypothesized that vehicle factors other than seat height can have substantial and independent effects on driver seat position. The effects of steering-wheel position, seat height, seat-cushion angle, and transmission type on driver fore/aft seat position are being investigated, and results are being used to develop a new driver seating accommodation model called SAM.

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.

The ASPECT program was conducted to develop new Automotive Seat and Package Evaluation and Comparison Tools. This paper presents a summary of the objectives, methods, and results of the program. The primary goal of ASPECT was to create a new generation of the SAE J826 H-point machine. The new ASPECT manikin has an articulated torso linkage, revised seat contact contours, a new weighting scheme, and a simpler, more user-friendly installation procedure. The ASPECT manikin simultaneously measures the H-point location, seat cushion angle, seatback angle, and lumbar support prominence of a seat, and can be used to make measures of seat stiffness. In addition to the physical manikin, the ASPECT program developed new tools for computer-aided design (CAD) of vehicle interiors. The postures and positions of hundreds of vehicle occupants with a wide range of body size were measured in many different vehicle conditions.

Current ATD positioning practices depend on seat track position, seat track travel range, and design seatback angle to determine appropriate occupant position and orientation for impact testing. In a series of studies conducted at the University of Michigan Transportation Research Institute, driver posture and position data were collected in forty-four vehicles. The seat track reference points currently used to position ATDs (front, center, and rear of the track) were found to be poor predictors of the average seat positions selected by small female, midsize male, and large male drivers. Driver-selected seatback angle was not closely related to design seatback angle, the measure currently used to orient the ATD torso. A new ATD Positioning Model was developed that more accurately represents the seated posture and position of drivers who match the ATD statures.

This paper presents a laboratory study of body dimensions, seated posture, and seatbelt fit for children weighing from 40 to 100 lb (18 to 45 kg). Sixty-two boys and girls were measured in three vehicle seats with and without each of three belt-positioning boosters. In addition to standard anthropometric measurements, three-dimensional body landmark locations were recorded with a coordinate digitizer in sitter-selected and standardized postures. This new database quantifies the vehicle-seated postures of children and provides quantitative evidence of the effects of belt-positioning boosters on belt fit. The data will provide guidance for child restraint design, crash dummy development, and crash dummy positioning procedures.

The ASPECT (Automotive Seat and Package Evaluation and Comparison Tools) program developed a new physical manikin for seat measurement and new techniques for integrating the seat measurements into the vehicle design process. This paper presents an overview of new concepts in vehicle interior design that have resulted from the ASPECT program and other studies of vehicle occupant posture and position conducted at UMTRI. The new methods result from an integration of revised versions of the SAE seat position and eyellipse models with the new tools developed in ASPECT. Measures of seat and vehicle interior geometry are input to statistical posture and position prediction tools that can be applied to any specified user population or individual occupant anthropometry.