A Computational Framework for Human-Centric Vehicular Crashworthiness Design and Decision-Making Under Uncertainty

Abstract

Government agencies, globally, strive to minimize the likelihood and frequency of human death and severe injury on road transport systems. From an engineering design standpoint, the minimization of these road accident effects on occupants becomes a critical design goal. This necessitates the quantification and management of injury risks on the human body in response to several vehicular impact variables and their associated uncertainties for different crash scenarios. In this paper, we present a decision-based, robust design (RD) framework to quantify and manage the impact-based injury risks on occupants for different computational model-based car crash scenarios. The key functionality offered is the designer's capability to conduct robust concept exploration focused on managing the selected impact variables and associated uncertainties, such that injury risks are controlled within acceptable levels. The framework's efficacy is tested for near-side impact scenarios with impact velocity and angle of impact as the critical variables of interest. Two injury criteria, namely, head injury criterion (HIC) and lateral neck injury criteria (Lateral Nij) are selected to quantitatively measure the head and neck injury risks in each crash simulation. Using the framework, a RD problem is formulated to determine the combination of impact variables that best satisfies the injury goals defined. The framework and associated design constructs are generic and support the formulation and decision-based robust concept exploration of similar problems involving models under uncertainty. Our focus in this paper is on the framework rather than the results per se.