  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.

Applications;Machine Learning