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In vehicle safety engineering, it is important to determine the severity of occupant injury during a crash. Computer simulations are widely used to study how occupants move in a crash, what they collide during the crash and thus how they are injured. The vehicle motion is typically defined for the occupant simulation by specifying a crash pulse. Many computer models used to analyze occupant kinematics do not calculate both vehicle motion and occupant motion at the same time. This paper presents a framework of response surface methodology for the crash pulse prediction and vehicle structure design optimization. The process is composed of running simulation at DOE sampling data points, generating surrogate models (response surface models), performing sensitivity analysis and structure design optimization for time history data (e.g., crash pulse).

Vehicle restraint systems are optimized to maximize occupant safety and achieve high safety ratings. The optimization formulation often involves the inclusion or exclusion of restraint features as discrete design variables, as well as continuous restraint design variables such as airbag firing time, airbag vent size, inflator power level, etc. The optimization problem is constrained by injury criteria such as Head Injury Criterion (HIC), chest deflection, chest acceleration, neck tension/compression, etc., which ensures the vehicle meets or exceeds all Federal Motor Vehicle Safety Standard (FMVSS) requirements. Typically, Genetic Algorithms (GA) optimizations are applied because of their capability to handle discrete and continuous variables simultaneously and their ability to jump out of regions with multiple local optima, particularly for this type of highly non-linear problems.

Driver injury probabilities in real-world frontal crashes were statistically modeled to estimate the relative roles of five variables of topical interest. One variable pertained to behavior (belt-wearing rate), one pertained to crash circumstances (speed change), and three pertained to occupant demographics (sex, age, and body mass index). The attendant analysis was composed of two parts: (1) baseline statistical modeling to help recover the past, and (2) sensitivity analyses to help consider the future. In Part 1, risk functions were generated from statistical analysis of real-world data pertaining to 1998-2014 model-year light passenger cars/trucks in 11-1 o’clock, full-engagement frontal crashes documented in the National Automotive Sampling System (NASS, 1997-2014). The selected data yielded a weighted estimate of 1,269,178 crash-involved drivers.