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

该项目将开发理论和算法工具，用于设计可证明安全的控制器，可以利用来自不同来源的预览信息。许多自主或半自主的网络物理系统（CPS）配备了提供预测未来窗口的机制。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，或者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，系统可以预览前面的内容。因此，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，对涉及国家利益的多种技术具有很大的前景。我们将把驾驶辅助系统作为互联汽车的主要应用。教育和推广活动将涉及本科生和研究生以及当地汽车公司的利益相关者。为了发展CPS的学习和预测安全理论，我们将：(i)开发一种建模形式，即预览自动机，用于具有预览信息和考虑弹性预测中的结构化不准确性的构造正确控制算法的系统；（ii）调查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边界的非合作主体行为的可信动态模型；（iv）从学习的动态模型中选择兼容模型的设计方法，以及在存在合作和非合作代理的情况下推导安全控制器的设计方法。我们的创新将使安全关键CPS能够充分利用传感、感知、通信和学习方面的新兴技术。该奖项反映了美国国家科学基金会的法定使命，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.