CPS: Small: Uncertainty-aware Framework for Specifying, Designing and Verifying Cyber-Physical Systems

该项目的目标是改进感知不确定性在信息物理系统（CPS）中的建模方式。复杂的自主CPS，从飞机和无人驾驶飞行器到未来的自动驾驶汽车，渗透到我们的日常生活中。这些系统由许多相互依赖的组件组成，在高度不确定的环境中运行，并表现出复杂的动态。这种相互依赖性不仅使它们的建模更加困难，而且也使量化它们的鲁棒性更加困难。单个未被发现的传感器读取错误、处理延迟或通信协议错误都可能导致灾难性事件，如飞机或汽车事故。这类事件可能导致生命损失以及公众的恐惧或信心丧失。该项目的方法是将不确定性视为时间的函数，而不是静态估计，这将使研究人员能够量化整个系统的稳健性。该项目的更广泛影响包括组织在南加州大学（USC）举行的无人机比赛。高度复杂的CPS设计的不确定性建模和鲁棒性推理是至关重要的。虽然事后分析需要通过冗余传感器（或传感器融合）改进传感技术或容错能力，但在本项目中，我们构建了数学和算法基础，以解决以下研究挑战：(1)时变不确定性的数学模型；(2)建立相互依存cps模型，分析相互依存和环境不确定性；(3)量化对这种不确定性的稳健性；(4)系统控制策略的设计。我们的方法是为这些复杂的相互关联的CPS模型开发一个基于时间逻辑的框架。利用统计学和信息论的概念，在时间逻辑的基础上扩充形式化规范技术，使我们的框架能够设计出具有适应性和弹性的高可信度CPS应用程序。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。

The goal of this project is to improve how perception uncertainty is modeled in Cyber-Physical Systems (CPS). Complex autonomous CPS, from airplanes and unmanned aerial vehicles to future self-driving cars, permeate our daily lives. These systems consist of many interdependent components operating in highly uncertain environments and exhibiting complex dynamics. This interdependency makes not only their modeling harder but also quantifying their robustness more difficult. A single undetected faulty reading in sensors, delay in processing or error in communication protocols can lead to catastrophic events such as airplane or car accidents. Such events can lead to loss of life as well as fear or loss of confidence in the public. The approach of this project is to consider uncertainty as a function of time, rather than static estimates, which will enable researchers to quantify the robustness of the overall system. The broader impacts of the project include organization of a drone competition to be held at the University of Southern California (USC). The modeling of uncertainty and reasoning about the robustness of highly complex CPS designs is crucial. While post-hoc analysis calls for improving sensing technology or fault tolerance through redundant sensors (or sensor fusion), in this project, we construct mathematical and algorithmic foundations to address research challenges in (1) mathematical models of time-varying uncertainty; (2) modeling of interdependent CPSs for analysis of interdependence as well as environment uncertainty; (3) quantification of robustness against such uncertainty; and (4) design of control strategies for these systems. Our approach is to develop a temporal logic-based framework for these complex interconnected CPS models. Augmenting formal specification techniques based on temporal logic with notions from statistics and information theory enables our framework to engineer high-confidence CPS applications that are adaptable and resilient.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.