Estimating the risk associated with transportation technology using multifidelity simulation
This provides a quantitative risk assessment method for transportation technology developers, though it appears incremental as it extends previous single-fidelity approaches.
The paper tackles the problem of estimating risk for transportation technology before deployment by proposing a multifidelity simulation method that combines low- and high-fidelity data, enabling inexpensive training and real-world generalization to evaluate candidate technologies early.
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.