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A previous paper on this topic presented the use of design of experiments (DOE) to evaluate the sensitivity of vehicle dynamics simulation of the postimpact motion of a vehicle that included high initial rotational rates. That investigation involved only one software package and thus was confined to one simulation model for the purposes of developing and refining the analysis method rather than including a variety of simulation models for broader application. This paper expands the application of the method to investigate the comparative behavior and sensitivity of several other vehicle dynamic simulation models commonly used in the field of crash reconstruction. The software packages included in the studies presented in this paper are HVE (SIMON and EDSMAC4), PC-Crash and VCRware. This paper will present the results of the study, conducted using DOE, involving these models.

The collection of research regarding occupant kinematics during low speed lateral vehicle-to-vehicle impacts is far less comprehensive than the much larger body of literature that quantifies the occupant kinematics associated with low speed rear end (longitudinal) impacts. In order to augment the available data, a series of 39 low speed far-side lateral vehicle-to-vehicle impacts were conducted in a laboratory setting. A combination of accelerometers and 3D motion tracking was used to characterize the motions of both the Target and Bullet vehicles during their collisions. The Target vehicle was initially stationary; the Bullet vehicle impacted the Target vehicle at the front passenger side door. The Bullet vehicle pre-impact speeds across all tests ranged from approximately 2.5 to 5.5 mph (4.0 to 8.9 kph; 1.1 to 2.5 m/s). Eight volunteers participated in the study.

This article presents an approach for comparing alternative repairable systems and calculating the value of information obtained by testing a specified number of such systems. More specifically, an approach is presented to determine the value of information that comes from field testing a specified number of systems in order to appropriately estimate the reliability metric associated with each of the respective repairable systems. Here the reliability of a repairable system will be measured by its failure rate. In support of the decision-making effort, the failure rate is translated into an expected utility based on a utility curve that represents the risk tolerance of the decision-maker. The algorithm calculates the change of the expected value of the decision with the sample size. The change in the value of the decision represents the value of information obtained from testing.

An important component of the process of the reconstruction of a vehicle crash involves the modeling of the motion of the vehicle(s) before and after a collision. Depending on the conditions, this motion might be modeled using a vehicle dynamics simulation program. In the simulated dynamics of vehicle motion, the tire forces are the predominant means by which the path of the vehicle is determined, with aerodynamic loads being the other force acting on the vehicle. Recent literature on this topic investigated the effect of the steer angle of the front wheels on the postimpact trajectory of a light vehicle for a large initial angular velocity. This paper looks more broadly at the modeling of light vehicle postimpact motion using vehicle dynamics simulation but for a wider range of factors. Design of experiments (DOE) is used to rank the effect of various physical factors of vehicle postimpact motion.

Design optimization occurs through a series of decisions that are a standard part of the product development process. Decisions are made anywhere from concept selection to the design of the assembly and manufacturing processes. The effectiveness of these decisions is based on the information available to the decision maker. Decision analysis provides a structured approach for quantifying the value of information that may be provided to the decision maker. This paper presents a process for determining the value of information that can be gained by evaluating linearly correlated design alternatives. A unique approach to the application of Bayesian Inference is used to provide simulated estimates in the expected utility with increasing observations sizes. The results provide insight into the optimum observation size that maximizes the expected utility when assessing correlated decision alternatives.