Estimating the risk associated with transportation technology using multifidelity simulation

Erik J. Schlicht, Nichole L. Morris

Research output: Contribution to journalArticle


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.
Original languageUndefined/Unknown
JournalICML Submission
StatePublished - Jan 30 2017


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