Matthew P. Reed

COMPARISON OF DRIVING PERFORMANCE ON-ROAD AND IN A LOW-COST SIMULATOR USING A CONCURRENT TELEPHONE DIALLING TASK

Driving performance in an instrumented vehicle was compared with performance in a low-cost, fixed-based driving simulator. Six men and six women drove a freeway route while periodically dialling simulated phone calls. The same subjects drove a laboratory driving simulator using two visual fidelity levels: a colour scene with relatively high detail, and a monochrome (night) scene showing only road-edge markings. Lane position, speed, steering-wheel angle and throttle position were recorded in both contexts. Lane-keeping in the simulator was less precise than on the road, but speed control performance was comparable. The SD of lane position in normal driving was about twice as large, on average, in the simulator (0.360 versus 0.165 m). Lane keeping and speed control were less precise when dialling the phone than in normal driving, both in the simulator and on the road, but the performance decrement was greater in the simulator. The addition of the phone task increased the mean lateral speed in the car by ab...

Methods for Measuring and Representing Automobile Occupant Posture

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.

An improved seating accommodation model with application to different user populations

In this paper 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 the following vehicle parameters are reflected in the prediction of mean seat position: seat height, steering-wheel-to-accelerator pedal distance, seat cushion angle, and transmission type. 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. (A) For the covering abstract of the conference see IRRD 492369.

Effects of vehicle interior geometry and anthropometric variables on automobile driving posture

The effects of vehicle package, seat, and anthropometric variables on posture were studied in a laboratory vehicle mockup. Participants (68 men and women) selected their preferred driving postures in 18 combinations of seat height, fore-aft steering wheel position, and seat cushion angle. Two seats differing in stiffness and seat back contour were used in testing. Driving postures were recorded using a sonic digitizer to measure the 3D locations of body landmarks. All test variables had significant independent effects on driving posture. Drivers were found to adapt to changes in the vehicle geometry primarily by changes in limb posture, whereas torso posture remained relatively constant. Stature accounts for most of the anthropometrically related variability in driving posture, and gender differences appear to be explained by body size variation. Large intersubject differences in torso posture, which are fairly stable across different seat and package conditions, are not closely related to standard anthropometric measures. The findings can be used to predict the effects of changes in vehicle and seat design on driving postures for populations with a wide range of anthropometric characteristics.

Optimizing Vehicle Occupant Packaging

Occupant packaging practice relies on statistical models codified in SAE practices, such as the SAE J941 eyellipse, and virtual human figure models representing individual occupants. The current packaging approach provides good solutions when the problem is relatively unconstrained, but achieving good results when many constraints are active, such as restricted headroom and sightlines, requires a more rigorous approach. Modeling driver needs using continuous models that retain the residual variance associated with performance and preference allows use of optimization methodologies developed for robust design. Together, these models and methods facilitate the consideration of multiple factors simultaneously and tradeoff studies can be performed. A case study involving the layout of the interior of a passenger car is presented, focusing on simultaneous placement of the seat and steering wheel adjustment ranges. Tradeoffs between adjustability, driver accommodation, and exterior vision are explored under this paradigm. These results are contrasted with those obtained using boundary manikins.

The HUMOSIM Ergonomics Framework: A New Approach to Digital Human Simulation for Ergonomic Analysis

The potential of digital human modeling to improve the design of products and workspaces has been limited by the time-consuming manual manipulation of figures that is required to perform simulations. Moreover, the inaccuracies in posture and motion that result from manual procedures compromise the fidelity of the resulting analyses. This paper presents a new approach to the control of human figure models and the analysis of simulated tasks. The new methods are embodied in an algorithmic framework developed in the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. The framework consists of an interconnected, hierarchical set of posture and motion modules that control aspects of human behavior, such as gaze or upper-extremity motion. Analysis modules, addressing issues such as shoulder stress and balance, are integrated into the framework. The framework encompasses many individual innovations in motion simulation algorithms, but the primary innovation is in the development of a comprehensive system for motion simulation and ergonomic analysis that is specifically designed to be independent of any particular human modeling system. The modules are developed as lightweight algorithms based on closed-form equations and simple numerical methods that can be communicated in written form and implemented in any computer language. The modules are independent of any particular figure model structure, requiring only basic forward-kinematics control and public-domain numerical algorithms. Key aspects of the module algorithms are “behavior-based,” meaning that the large amount of redundancy in the human kinematic linkage is resolved using empirical models based on laboratory data. The implementation of the HUMOSIM framework in human figure models will allow much faster and more accurate simulation of human interactions with products and workspaces using high-level, task-based control. INTRODUCTION Digital human figure models (DHM) are now widely used for ergonomic analysis of products and workplaces. In many organizations, DHM software is a tool of first resort for answering questions relating to physical interaction between people and objects. Yet any objective appraisal of the technology would conclude that the current reality of DHM software capability is far from the promise of a “digital human” that can interact realistically with products and environments. This paper is focused on efforts to improve the ability of DHM software to simulate physical posture and motion. Nearly every other aspect of DHM functionality also warrants improvement, including body shape representation, strength simulation, and cognitive function, but posture and motion are critical to the primary applications of DHM to the assessment of physical tasks. Posture simulation is as old as computerized manikins, because the manikin must be postured before an analysis can be conducted. Important early work was performed by Ryan for the U.S. Navy (Ryan 1970). Porter et al. (1993) summarized applications of digital human models in vehicle ergonomics during the early years of personal computers, at which time few of the current commercial DHM software tools were in use. Chaffin (2001) presented case studies of the expanding use of DHM for both product and workplace design and assessment. As evidence of the importance of posture and motion simulation, dozens of papers in the SAE literature and in other forums have presented a wide variety of methods for human simulating postures and motions, including multiple-regression (Snyder et al. 1972); analytic and numerical inverse kinematics (Jung et al. 1995; Tolani et al. 2000); optimization-based inverse kinematics (Wang and Verriest 1998); differential inverse kinematics (Zhang and Chaffin, 2000); functional regression on stretch-pivot parameters (Faraway 2000); scaling, warping, and blending of motion-capture data (Park et al. 2002; Faraway 2003; Monnier et al. 2003; Park et al. 2004; Dufour and Wang 2005); and many 2006-01-2365 The HUMOSIM Ergonomics Framework: A New Approach to Digital Human Simulation for Ergonomic Analysis Matthew P. Reed, Julian Faraway, Don B. Chaffin and Bernard J. Martin University of Michigan

Optimizing Truck Cab Layout for Driver Accommodation

One important source of variability in the performance and success of products designed for use by people is the people themselves. In many cases, the acceptability of the design is affected more by the variability in the human users than by the variability attributable to the hardware from which the product is constructed. Designing for human variability as an inherent part of the product optimization process can improve the overall performance of the product. This paper presents a new approach to artifact design that applies population sampling and stochastic posture prediction in an optimization environment to achieve optimal designs that are robust to variability among users, including differences in age, physical size, strength, and cognitive capability. A case study involving the layout of the interior of a heavy truck cab is presented, focusing on simultaneous placement of the seat and steering-wheel adjustment ranges. Trade-offs between adjustability (an indicator of cost), driver accommodation, and safety are explored under this paradigm.

Comparison of Child Body Dimensions with Rear Seat Geometry

Children who are too large for harness restraints but too small to obtain good restraint from a vehicle seatbelt alone should be seated in a belt-positioning booster. Boosters have been shown to significantly reduce abdominal injuries caused by seatbelts. This effectiveness may be due in part to the fact that boosters reduce the effective seat cushion length, allowing children to sit more comfortably without slouching. NHTSA recommends that children who do not use harness restraints use boosters until they are at least 145 cm tall. In this paper, data from several sources were combined to assess how well children fit on rear seat cushions. Data from NASS-GES were analyzed to determine the age distribution of rear-seat occupants. Anthropometric data from several sources were analyzed to determine the distribution of buttockpopliteal length, a measure of thigh length that is a key determinant of seat fit, as a function of age and gender. Secondand third-row cushion lengths were measured on a convenience sample of 56 late-model vehicles. Comparing the distribution of body size for rear-seat occupants with the seat cushion lengths showed that most cushions are too long for most rear-seat occupants, using commonly applied standards of seat fit. Given that most rear-seat occupants in the U.S. are children, rear-seat design standards should consider the smaller body dimensions and different restraint needs of this population.

A New Approach to Modeling Driver Reach

The reach capability of drivers is currently represented in vehicle design practice in two ways. The SAE Recommended Practice J287 presents maximum reach capability surfaces for selected percentiles of a generic driving population. Driver reach is also simulated using digital human figure models. In typical applications, a family of figure models that span a large range of the target driver population with respect to body dimensions is positioned within a digital mockup of the drivers workstation. The articulated segments of the figure model are exercised to simulate reaching motions and driver capabilities are calculated from the constraints of the kinematic model. Both of these current methods for representing driver reach are substantially limited. The J287 surfaces are not configurable for population characteristics, do not provide the user with the ability to adjust accommodation percentiles, and do not provide any guidance on the difficulty of reaches that are attainable. The figure model method is strongly dependent on the quality of the models used for posturing and range of motion, and, in any case, cannot reliably generate population distributions of either reach capability or difficulty. A new method of modeling driver reach capability is presented. The method is based on a unified model of reach difficulty and capability in which a maximum reach ia a maximally difficult reach. The new approach is made possible by new measurement methods that allow detailed and efficient sampling of an individuals reach-difficulty function. This paper summarizes the experimental approach and presents the structure of the new integrated model of population reach difficulty and capability.

DEVELOPMENT OF AN IMPROVED DRIVER EYE POSITION MODEL

Society of Automotive Engineers (SAE) Recommended Practice J941 describes the eyellipse, a statistical representation of driver eye locations. 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 (vehicle seating reference point) 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 United States population. Data were collected in thirty-three vehicles. Significant and consistent differences were observed between eye position data collected before and after driving, indicating that actual driving is an important protocol feature for accurate measurement of driver eye position. In six vehicles, eyellipses obtained with two-way and six-way seat track travel were only slightly different. On average, drivers select seatback angles that are about 1.6 degrees more upright than design seatback angles. Stepwise regression techniques were used to identify the vehicle variables that have important effects on the distribution of driver eye locations. (A) For the covering abstract of the conference see IRRD 492369.