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.

翻译：测试与评估是智能网联汽车（CAVs）开发中昂贵但关键的环节。本文提出一种自适应采样框架，用于高效评估CAVs的事故率，尤其适用于场景测试——其中输入参数的概率分布由自然驾驶数据已知。该框架基于代理模型近似CAV性能，并通过信息论考量设计新型采集函数，最大化下一个样本的收益（即对事故率的信息增益）。除仅使用单一高保真度CAV性能模型的标准应用外，我们还将该方法扩展至双保真度场景：通过引入计算成本更低的低保真模型近似CAV性能。针对第二种情形，我们构建的方法允许从保真度层级（即选择使用哪个模型）和采样位置两方面确定下一个样本，以最大化单位成本的收益。该框架在广为研究的CAV二维切入问题中进行了测试，其中采用不同时间分辨率的智能驾驶模型（IDM）构建高保真度和低保真度模型。结果表明，针对同一问题，单保真度方法优于现有方法；而双保真度方法在达到相近事故率估计精度时，可进一步节省一半计算成本。