Autonomy and mobility simulation time reduction through machine learning while considering uncertainty and reliability prediction

Modeling and simulation (M&S) tools are used extensively throughout GVSC and in the Army in order to perform analysis of ground vehicles more quickly and less expensively than through physical testing. The CREATE-GV project is one such M&S software effort that focuses on mobility and autonomous vehicle simulation and analysis, using physics- based 3-dimensional modeling in order to accurately calculate a variety of ground vehicle metrics and parameters of interest. However, because these simulations are high-fidelity, they often require a great deal of computational power and time. One approach to reducing simulation time that has proved effective in certain contexts is the creation of "surrogate models" through machine learning (ML) algorithms. However, it is often very challenging to accurately predict the mobility of a ground vehicle system in general, and there is no existing model that can predict the mobility of autonomous systems. A great deal of uncertainty exists in the mobility and autonomy area of physics-based simulation models related to modeling assumptions, terrain conditions, and insufficient knowledge related to interactions between the vehicle and terrain. Understanding how the uncertainties inherent in autonomous mobility prediction affect model accuracy is still an open fundamental research question. In this work, we present a surrogate modeling approach leveraging machine learning algorithms to work with CREATE-GV in order to increase the computation speed of the mobility assessments, while still considering the reliability of the mobility predictions under uncertainty.