An adaptive multi-fidelity sampling framework for safety analysis of connected and automated vehicles

TL;DR

This paper introduces an adaptive multi-fidelity sampling framework that efficiently estimates CAV accident rates using surrogate models and novel acquisition functions, significantly reducing computational costs in safety testing.

Contribution

It develops a new adaptive sampling method incorporating bi-fidelity models for efficient CAV safety evaluation, extending traditional single-fidelity approaches.

Findings

Single-fidelity method outperforms existing approaches.

Bi-fidelity method halves computational cost for similar accuracy.

Framework effectively estimates accident rates in scenario-based tests.

Abstract

Testing and evaluation are expensive but critical steps in the development of connected and automated vehicles (CAVs). In this paper, we develop an adaptive sampling framework to efficiently evaluate the accident rate of CAVs, particularly for scenario-based tests where the probability distribution of input parameters is known from the Naturalistic Driving Data. Our framework relies on a surrogate model to approximate the CAV performance and a novel acquisition function to maximize the benefit (information to accident rate) of the next sample formulated through an information-theoretic consideration. In addition to the standard application with only a single high-fidelity model of CAV performance, we also extend our approach to the bi-fidelity context where an additional low-fidelity model can be used at a lower computational cost to approximate the CAV performance. Accordingly, for the second case, our approach is formulated such that it allows the choice of the next sample in terms of both fidelity level (i.e., which model to use) and sampling location to maximize the benefit per cost. Our framework is tested in a widely-considered two-dimensional cut-in problem for CAVs, where Intelligent Driving Model (IDM) with different time resolutions are used to construct the high and low-fidelity models. We show that our single-fidelity method outperforms the existing approach for the same problem, and the bi-fidelity method can further save half of the computational cost to reach a similar accuracy in estimating the accident rate.