Cyber-Physical Systems (CPS) continuously interact with their physical environments through embedded software controllers that observe the environments and determine actions. Field Operational Tests (FOT) are essential to verify to what extent the CPS under analysis can achieve certain CPS goals, such as satisfying the safety and performance requirements, while interacting with the real operational environment. However, performing many FOTs to obtain statistically significant verification results is challenging due to its high cost and risk in practice. Simulation-based verification can be an alternative to address the challenge, but it still requires an accurate virtual environment model that can replace the real environment interacting with the CPS in a closed loop.

In this article, we propose ENVI (ENVironment Imitation), a novel approach to automatically generate an accurate virtual environment model, enabling efficient and accurate simulation-based CPS goal verification in practice.To do this, we first formally define the problem of the virtual environment model generation and solve it by leveraging Imitation Learning (IL), which has been actively studied in machine learning to learn complex behaviors from expert demonstrations. The key idea behind the model generation is to leverage IL for training a model that imitates the interactions between the CPS controller and its real environment as recorded in (possibly very small) FOT logs. We then statistically verify the goal achievement of the CPS by simulating it with the generated model. We empirically evaluate ENVI by applying it to the verification of two popular autonomous driving assistant systems. The results show that ENVI can reduce the cost of CPS goal verification while maintaining its accuracy by generating accurate environment models from only a few FOT logs. The use of IL in virtual environment model generation opens new research directions, further discussed at the end of the article.

信息物理系统 (CPS) 通过观察环境并确定操作的嵌入式软件控制器持续与其物理环境交互。现场操作测试 (FOT) 对于验证所分析的 CPS 在与真实操作环境交互的同时能够在多大程度上实现某些 CPS 目标（例如满足安全和性能要求）至关重要。然而，由于实践中的成本和风险较高，执行多次 FOT 以获得统计上显着的验证结果具有挑战性。基于仿真的验证可以作为解决这一挑战的替代方案，但它仍然需要一个准确的虚拟环境模型来代替与闭环中的 CPS 交互的真实环境。

在本文中，我们提出了 ENVI（ENVironment Imitation），这是一种自动生成精确虚拟环境模型的新方法，可在实践中实现高效、准确的基于仿真的 CPS 目标验证。为此，我们首先正式定义虚拟环境的问题环境模型生成并通过利用模仿学习（IL）来解决它，它在机器学习中得到了积极的研究，可以从专家演示中学习复杂的行为。模型生成背后的关键思想是利用 IL 来训练模型，该模型模仿 CPS 控制器与其真实环境之间的交互（记录在（可能非常小）FOT 日志中）。然后，我们通过使用生成的模型进行模拟来统计验证 CPS 的目标实现情况。我们通过将 ENVI 应用于两种流行的自动驾驶辅助系统的验证来对 ENVI 进行实证评估。结果表明，ENVI 可以通过仅从少量 FOT 日志生成准确的环境模型来降低 CPS 目标验证的成本，同时保持其准确性。IL 在虚拟环境模型生成中的使用开辟了新的研究方向，文章末尾将进一步讨论。