Search Results

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.

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.

The SAE Controls Reach Study provided the opportunity to obtain a considerable amount of driver work space data in addition to hand and foot reach. A side-view film recording was made of each test subject assuming a driving attitude and looking straight ahead. Of concern were locations for the driver's eyes, top of head, back of head, knee, and seat. These data were combined with other data previously reported to develop tools that will aid the designer in describing space and comfort requirements for seated drivers and passengers in automotive packages. Fixed-seat eye ellipses, head locations, shin-knee locations, and preferred seat positions are described.

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 (Automotive Seat and Package Evaluation and Comparison Tools) manikin provides new capabilities for vehicle and seat measurement while maintaining continuity with previous practices. This paper describes how the manikin is used in the development of new designs, the audit verification of build, and in benchmarking competitive vehicles and seats. The measurement procedures are discussed in detail, along with the seat and package dimensions that are associated with the new tool.

The primary objective of the ASPECT (Automotive Seat and Package Evaluation and Comparison Tools) program was to develop a new generation of the SAE J826 H-point manikin. The new ASPECT manikin builds on the long-term success of the H-point manikin while adding new measurement capability and improved ease of use. The ASPECT manikin features an articulated torso linkage to measure lumbar support prominence; new contours based on human subject data; a new weighting scheme; lightweight, supplemental thigh, leg, and shoe segments; and a simpler, user-friendly installation procedure. This paper describes the new manikin in detail, including the rationale and motivation for the design features. The ASPECT manikin maintains continuity with the current SAE J826 H-point manikin in important areas while providing substantial new measurement capability.

This study was conducted to resolve discrepancies and fill in gaps in the biomechanical impact response of the human abdomen to frontal impact loading. Three types of abdominal loading were studied: rigid-bar impacts, seatbelt loading, and close-proximity (out-of-position) airbag deployments. Eleven rigid-bar free-back tests were performed into the mid and upper abdomens of unembalmed instrumented human cadavers using nominal impact speeds of 6 and 9 m/s. Seven fixed-back rigid-bar tests were also conducted at 3, 6, and 9 m/s using one cadaver to examine the effects of body mass, spinal flexion, and repeated testing. Load-penetration corridors were developed and compared to those previously established by other researchers. Six seatbelt tests were conducted using three cadavers and a peak-loading rate of 3 m/s. The seatbelt loading tests were designed to maximize belt/abdomen interaction and were not necessarily representative of real-world crashes.

This study continued the biomechanical investigations of forearm fractures caused by direct loading of steering-wheel airbags during the early stages of deployment. Twenty-four static deployments of driver airbags were conducted into the forearms of unembalmed whole cadavers using a range of airbags, including airbags that are depowered as allowed by the new federal requirements for frontal impact testing. In general, the depowered airbags showed a reduction in incidence and severity of forearm fractures compared to the pre-depowered airbags tested. Data from these twenty-four tests were combined with results from previous studies to develop a refined empirical model for fracture occurrence based on Average Distal Forearm Speed (ADFS), and a revised value for fifty-percent probability of forearm-bone fracture of 10.5 m/s. Bone mineral content, which is directly related to forearm tolerance, was found to be linearly related to arm mass.