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Engine control units (ECUs) on heavy trucks have been capable of storing “last stop” or “hard stop” data for some years. These data provide useful information to accident reconstruction personnel. In past studies, these data have been analyzed and compared to higher-resolution on-vehicle data for several heavy trucks and several makes of passenger cars. Previous published studies have been quite helpful in understanding the limitations and/or anomalies associated with these data. This study was designed and executed to add to the technical understanding of heavy truck event data recorders (EDR), specifically data associated with a modern Cummins power plant ECU. Emergency “full-treadle” stops were performed at many combinations of load-speed-surface coefficient conditions. In addition, brake-in-curve tests were performed on wet Jennite for various conditions of disablement of the braking system.

Visual acuity is defined as the spatial resolution capacity of the visual system. It is commonly accepted that the foveal, or on-center visual acuity, is far better than the peripheral acuity due to retinal and cortex constructions [Anstis, 1998]. Other factors affect the way an individual visually perceives their surroundings as well. This paper seeks to illustrate the variance of the visual field in terms of several compounding factors including acuity, depth perception, and color perception and their effect on a vehicle operator's visual field. Within accident reconstruction, digital renderings of an accident are often re-created from the vantage point of one or more persons to illustrate what they may have perceived prior to and during an event. However, typically no compensations are made to account for any general and/or individual visual based sensory degradation.

During a motor-vehicle collision, an occupant may interact with a variety of interior structures. The material properties and construction of these structures can directly affect the occupant's kinetic response. Simulation tools such as MADYMO (Mathematical Dynamical Models) can be used to estimate the forces imparted to an occupant for injury mechanism and causation evaluation relative to a particular event. Depending on the impact event and the specific injury mechanism being evaluated, the selection of proper material characteristics can be quite important. A comprehensive literature review of MADYMO studies illustrates the prevalent use of generic material characteristics and the need for improved property estimation and implementation methods.

This paper presents a parametric study that utilized the CVS/ATB multi-body simulation program to investigate the interaction of the hand and wrist with a door handgrip during side air bag loading. The goal was to quantify the relative severity of various hand and handgrip positions as a guide in the selection of a test matrix for laboratory testing. The air bag was represented as a multi-body system of ellipsoidal surfaces that were created to simulate a prototype seat-mounted thorax side air bag. All simulations were set in a similar static test environment as used in corresponding dummy and cadaver side air bag testing. The occupant mass and geometric properties were based on a 5th percentile female occupant in order to represent a high-risk segment of the adult population. The upper extremity model consisted of wrist and forearm rotations that were based on human volunteer data.

Although filter class specifications have been defined for most anthropomorphic test devices, no recommendation exists for the instrumented upper extremity. A three-part study was performed to determine the best channel filter class (CFC) to use for the instrumented upper extremity. By analyzing frequency content of signals from accelerometers and load cells, filtering data through three of the four possible CFC's to compare effects on the signals, and performing an injury comparison between cadaver data and the filtered load cell data, CFC 600 was chosen and recommended as the optimum filter class to use for upper extremity testing.