CPS: Medium: Collaborative Research: Data-Driven Modeling and Preview-Based Control for Cyber-Physical System Safety

该项目将开发用于设计可证明安全的控制器的理论和算法工具，这些控制器可以利用来自不同来源的预览信息。许多自主或半自主的信息物理系统（CPS）都配备了一种机制，可以提供一个预测未来的窗口。这些机制可以是前瞻性传感器，如摄像头（以及相应的感知算法）、地图信息、预测信息或从数据中学习的外部代理的更复杂的预测模型。通过这些机制，在运行时，系统可以预览未来的情况。利用这些信息来提高CPS的性能，同时保持对其安全性的强有力保证，因此，对于国家利益的多种技术来说，这是一个巨大的希望。我们将在联网车辆中使用驾驶员辅助系统作为主要应用。教育和推广活动将涉及本科生和研究生沿着当地汽车公司的利益相关者。为了开发CPS的学习和预测支持的安全性理论，我们将：（i）开发一种建模形式主义，即预览自动机，用于具有预览信息和构造校正控制算法的系统，这些算法考虑了弹性预测中的结构化不准确性;（ii）研究合作如何有助于丰富预览信息;（iii）通过有限样本数据分析，学习具有可证明的不确定性界限的非合作代理行为的可信动态模型;以及（iv）用于从所学习的动态模型中选择兼容模型以及用于在存在协作和非协作的情况下导出安全控制器的设计方法。合作代理人。我们的创新将使安全关键型CPS充分利用传感、感知、通信和学习方面的新兴技术。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will develop the theory and algorithmic tools for the design of provably-safe controllers that can leverage preview information from different sources. Many autonomous or semi-autonomous cyber-physical systems (CPS) are equipped with mechanisms that provide a window of projecting into the future. These mechanisms can be forward looking sensors like cameras (and corresponding perception algorithms), map information, forecast information, or more complicated predictive models of external agents learned from data. Through these mechanisms, at run-time, the systems have a preview of what lies ahead. Leveraging this information to improve performance of CPS while keeping strong guarantees on their safety, therefore, holds great promise for multiple technologies of national interest. We will use driver-assist systems in connected vehicles as the main application. Education and outreach activities will involve undergraduate and graduate students along with stakeholders from local automotive companies.^To develop the theory for learning- and prediction-enabled safety for CPS we will: (i) develop a modeling formalism, namely preview automata, for systems with preview information and correct-by-construction control algorithms that consider structured inaccuracies in the predictions for resilience; (ii) investigate how cooperation can assist in enriching the preview information; (iii) learn, via finite-sample data analysis, trustworthy dynamical models of the behaviors of non-cooperative agents with provable uncertainty bounds; and (iv) design methods for selecting compatible models from the learned dynamical models and for deriving safe controllers in the presence of cooperative and non-cooperative agents. Our innovations will enable safety-critical CPS to take full advantage of emerging technologies on sensing, perception, communication, and learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.