This paper provides a quantitative method for estimating the risk associated with candidate transportation technology, before it is developed and deployed. The proposed solution extends previous methods that rely exclusively on low-fidelity human-in-the-loop experimental data, or high-fidelity traffic data, by adopting a multifidelity approach that leverages data from both low- and high-fidelity sources. The multifidelity method overcomes limitations inherent to existing approaches by allowing a model to be trained inexpensively, while still assuring that its predictions generalize to the real-world. This allows for candidate technologies to be evaluated at the stage of conception, and enables a mechanism for only the safest and most effective technology to be developed and released.
@article{arxiv.1701.08588,
title = {Estimating the risk associated with transportation technology using multifidelity simulation},
author = {Erik J. Schlicht and Nichole L. Morris},
journal= {arXiv preprint arXiv:1701.08588},
year = {2017}
}