Robust Truck Cabin Layout Optimization Using Advanced Driver Variance Models

One important source of variance 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 variance in the human users than by the variance attributable to the hardware from which the product is constructed. Consequently, optimization of products used by people may benefit from consideration of human variance through robust design methodologies. We propose that design under uncertainty methodologies can be utilized to generate designs that are robust to variance among users, including differences in age, physical size, strength, and cognitive capability. Including human variance as an inherent part of the product optimization process will improve the overall performance of the product (be it comfort, maintainability, cognitive performance, or other metrics of interest) and could lead to products that are more accessible to broader populations, less expensive, and safer. 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. Tradeoffs between adjustability/cost, driver accommodation, and safety are explored under this paradigm. INTRODUCTION Humans are highly variable on many functional measures that are related to artifact design variables. The wide ranges of adult standing height, hip breadth, and other body dimensions are readily observed and often considered quantitatively in design. Variability in human perception, behavior, and performance can be equally or more important than dimensional variability, but these factors are less commonly considered in a quantitative manner. Human adaptability diminishes but does not eliminate the impact of inter-individual variability on artifact performance. The ubiquity of “one-size-fits-all” is a testament to adaptability, but is not a prescription for good design, particularly in cases where performance is important and people interact with the artifact through multiple interfaces. Designing for people requires the quantitative consideration of all relevant aspects of human variability. The design of a vehicle interior is one problem in which human (occupant) variability is a primary concern. The layout of the driver’s workstation in a truck cab includes the selection of locations for the seat, steering wheel, pedals, and other components, subject to boundary constraints (such as floor height, roof height, firewall position, and cab length). Within these and other 1 Copyright c © 2005 by ASME Figure 1. A typical cab “driver packaging” problem involves the design of the interior environment so that a large population of drivers is accommodated. The accelerator heel point (AHP) is a fiducial point to which other cab components are referenced. constraints, the vehicle interior is engineered to maximize the accommodation of the design population, where accommodation means that a person is able to perform all required tasks while seated in a comfortable posture. A person is usually considered to be accommodated as a driver if he or she can choose component locations and a posture without encountering the limits of adjustment ranges [1]. However, even among accommodated individuals, a vehicle usually provides a wide range of performance on other important measures, such as headroom and exterior vision. During the vehicle design process, a common driver accommodation problem is the selection of the position and size of the seat adjustment range (fore-aft and vertical) with respect to the pedals such that a target percentage of the population is accommodated (Figure 1). The problem is more complicated if both the seat and steering wheel are adjustable (Figure 2). Very large adjustment ranges for all components would accommodate nearly all drivers, but adjustability is constrained by cost, safety, and the desire to reduce cab dimensions to maximize cargo capacity. Frequently, adjustment ranges are limited by carry-over components from current-production vehicles or by a requirement to use commercial, off-the-shelf (COTS) hardware. In this case, the design problem can be simplified to selecting the locations for fixed adjustment ranges, which entails selecting values for four variables defining the fore-aft and vertical positions of the center of the seat adjustment range and the steering-wheel pivot point. Current industry practice for vehicle interior packaging relies on two toolsets. The Society of Automotive Engineers (SAE) maintains a set of Recommended Practices that define methods and models for component layout [2]. For example, SAE J1517 describes the preferred fore-aft position for seat adjustment ranges as a function of seat height. Vehicle designers also make extensive use of digital human modeling (DHM) software, Figure 2. Adjustability in both the steering wheel and the seat increases accommodation. The adjustment ranges depicted are larger than typical values to improve the clarity of the illustration which places software manikins representing drivers into digital vehicle mockups [3]. DHM software can represent people with a wide range of body dimensions in many possible postures. Virtual environments have progressed and are being used for conceptual layouts. They are, however, insufficient for more refined ergonomics assessments [4]. For purposes of physical accommodation, human variance can usefully be partitioned into dimensional (anthropometric) and behavioral variability. For example, the height of a driver’s eyes above the seat is related to both torso length and torso posture. The most common approach to representing anthropometric variabilty is the use of manikins or templates that represent people at dimensional extremes. The (often implicit) rationale for using only a few ”boundary manikins” is that designs accommodating the anthropometric extremes (for example, an average woman who is 5th-percentile by stature and an average man who is 95th-percentile by stature) will also accommodate people with less-extreme dimensions. Many contemporary research publications approach design problems in this manner, including methods for optimizing workspaces and controls in aircraft cockpits [5–7]. The selection of anthropometric extreme cases has been extended to the use of many boundary manikins selected with consideration of anthropometric covariance [8]. For example, the A-CADRE family of 17 manikins represents much of the multivariate anthropometric variability in an adult population [9]. Boundary-manikin sampling approaches are commonly used with DHM software to create figure models. Any use of manikins in design requires that they be postured in realistic ways. Posturing is often performed manually by the designer, but a number of approaches to posturing driver manikins have been developed [10–12]. The resulting postures are related by statistical models to data gathered from drivers in a variety of laboratory and vehicle configurations. Manikin posturing algorithms are usually deterministic, giving a single 2 Copyright c © 2005 by ASME Optimization Design Variables cab dimensions, adjustment ranges, component locations, etc. factors such as maximum permissable cab height, maximum adjustment ranges, etc. Vehicle Constraints fixed dimensions based on, for example, carry-over chassis or body features Vehicle Parameters gender mix means and covariance matrix for body dimensions Driver Population Model combination of such factors as: accommodation (e.g. seat and steering wheel placement) comfort (e.g. headroom) safety (e.g. steering wheel clearance and exterior vision) Posturing Model preference for component locations as a function of body size effects of restrictions due to component locations (censoring) random variance unrelated to body and cab dimensions Population Objective Function Sample from Population Figure 3. Schematic of optimization methodology, showing submodels and information flow. posture for a particular combination of manikin body dimensions and task constraints. However, people who have the same body dimensions often drive with substantially different postures [10, 12, 13]. As a consequence, manikin-based design approaches, even with ideally accurate posture prediction, are insufficient for quantitative assessment of accommodation [12,14]. The effects of postural variance that is unrelated to body dimensions must be taken into account in vehicle design. The SAE Recommended Practices for vehicle design accomplish this by the use of unified statistical models that encompass population variance in both body dimensions and behavior. For example, the eyellipse (SAE J941) approximates the distribution of driver eye locations in vehicle space as a three-dimensional normal distribution. Because it models eye location directly, rather than attempting to predict it from the combined effects of anthropometric and postural variability, the eyellipse has been one of the most elegant and effective tools ever developed for human factors analysis [1]. A recent update to J941 replaced the original model for passenger cars from the 1960s with a more flexible model developed in modern vehicles [15]. Unfortunately, the versions of the J941 driver eyellipse and the seating accommodation model in J1517 that are applicable to trucks and buses (SAE Class B) are substantially out-of-date and limited in ways that make them inadequate for many design situations. First, the driver population used to develop those models differs substantially from current driver populations with respect to anthropometric variables, particularly those related to body weight. Second, the SAE models do not take into account the large range of adjustability common on modern trucks, particularly steering wheel tilt/telescope and seat height adjustment. Third, the SAE tools are essentially univariate, dealing with only one variable at time (fore-aft seat position or eye location). The SAE Recommended Practices do not provide any way to consider, for example, the effects of restricted seat adjustment range on eye location. New vehicle interior design methods are needed that allow simultaneous consideration of multiple constraints and objectives, while preserving the quantitative rigor associated with the SAE occupant packaging tools. This paper presents a new approach to vehicle interior design that applies population sampling and stochastic posture prediction in an optimization environment to achieve optimal designs that are robust to human variability.