Approach for machine learning based design of experiments for occupant simulation

Abstract

The complexity of crash scenarios in the context of vehicle safety is steadily increasing. This is especially the case on the way to mixed traffic challenges with non-automated and automated driving vehicles. The number of simulations required to design a robust restraint system is thus also increasing. The vast range of possible scenarios here is causing a huge parameter space. Simultaneously biofidelic simulation models are resulting in very high computational costs and therefore the number of simulations should be limited to a feasible operational range. In this study, a machine-learning based design of experiments algorithm is developed, which specifically addresses the issues when designing a safety system with a limited number of simulation samples taking diversity of the occupant and accident scenario into account. In contrast to an optimization task, where the aim is to meet a target function, our job has been to find the critical load case combinations to make sure that these are addressed and not missed. A combination of a space-filling approach and a metamodel has been established to find the critical scenarios in order to improve the system for those cases. It focuses specifically on the areas that are difficult to predict by the metamodel. The developed method was applied to iteratively generate a simulation matrix of a total of 208 simulations with a generic interior model and a detailed FE human body model. Kinematic and strain-based injury metrics were used as simulation output. These were used to train the metamodels after each iteration and derive the simulation matrix for the next iteration. In this paper we present a method that allows the training of a robust metamodel for the prediction of injury criteria, considering both varying load cases and varying restraint system parameters for individual anthropometries and seating postures. Based on that, restraint systems or metamodels can be optimized to achieve the best overall performance for a huge variety of possible scenarios with a specific focus on critical scenarios.