Multi-objective optimization of vehicle occupant restraint system by using evolutionary algorithm with response surface model

Abstract

This research reports a vehicle occupant restraint system design by using evolutionary multi-objective optimization with response surface model. The vehicle occupant restraint systems are composed of restraint equipment, such as an airbag, a seat belt and a knee bolster. The optimization aims to improve the safety of the system by evaluating some indexes based on some safety regulations. Estimation models of the safety indexes are introduced for accelerating the optimization. The estimation models, which are called the response surface models, are constructed by using Gaussian Process, which is a kind of machine learning method. The Gaussian Process constructs the estimation model from sampling results, which are calculated by using multi-body dynamics simulation. Some helpful information for designing the restraint systems, such as trade-off information of safety performance and contribution of design variables for the safety performance, is obtained by analysing the Pareto optimal solutions.