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

arXiv: https://arxiv.org/abs/2210.14114 · HTML: https://mlsanity.com/paper/2210.14114/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2022.

## Built on by (most cited first, 5 of 5)

- [Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems](https://mlsanity.com/paper/2501.11924/) (2025)
- [Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning](https://mlsanity.com/paper/2402.19275/) (2024)
- [LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization](https://mlsanity.com/paper/2412.00517/) (2024)
- [VLM as Strategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion](https://mlsanity.com/paper/2512.02844/) (2025)
- [A generalized likelihood-weighted optimal sampling algorithm for rare-event probability quantification](https://mlsanity.com/paper/2310.14457/) (2023)

## Abstract (arXiv)

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.