# Estimating the risk associated with transportation technology using multifidelity simulation

## TL;DR

This paper introduces a multifidelity simulation approach to estimate transportation technology risks early in development, combining low- and high-fidelity data for accurate, cost-effective predictions.

## Contribution

It extends existing risk estimation methods by integrating multifidelity data, enabling early, reliable assessment of transportation technologies before deployment.

## Key findings

- Multifidelity approach improves risk prediction accuracy.
- Method reduces costs compared to high-fidelity-only models.
- Enables early-stage technology evaluation.

## Abstract

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.