Artificial road input data synthesis: a full vehicle model case study

Abstract

In order to reduce the time and cost of developing a vehicle, it is important that virtual durability testing is carried out. In this research project, the aim is to predict the road input for the virtual durability test simulation of a new vehicle variant by transforming the data from a predecessor model using the vehicle's configuration parameters to generate a new and representative road input. To achieve this, a full vehicle model of a sport utility vehicle (SUV) is developed and validated with test data collected on a proving ground; this model is used to generate data to train and validate a NARX based artificial neural network (ANN) tool which is then subsequently used to predict the road input to the new variant of the vehicle. The use of ANNs in this project shows one of the many potentials of artificial intelligence in developing virtual capabilities within the automotive industry.