Collaborative Research: CPS: Small: Risk-Aware Planning and Control for Safety-Critical Human-CPS

网络物理系统的未来是智能技术，可以与人类协同、合作、安全地工作。智能技术和人类将共享自主权，即共享控制权的权利、义务和能力，以实现他们在运营环境中的共同目标。例如，手术机器人必须与外科医生互动，以提高他们进行高精度手术的能力，无人机需要向人类和地方运送包裹，自动驾驶汽车需要与人类驾驶的汽车共享道路。在所有这些相互作用中，这些系统必须安全运行，尽管这些风险和不确定性是人类、技术和它们相互作用的环境所固有的。该项目的关键见解是，在人类需要与能够自主或半自主行动的网络物理系统（CPS）密切互动的情况下，可以开发控制策略，以提高安全性。该项目的目标是开发风险感知交互式控制和规划，以实现安全的网络-物理-人（CPS-h）系统。本项目将主要从三个方面推动CPS-h规划和控制的发展：(i)开发适合一般自主CPS-h的计算可处理的风险感知轨迹规划算法，（ii）开发计算高效且经验支持的框架，以考虑人类决策中的风险意识，以及（iii）派生交互感知规划算法，以实现多个风险感知代理之间的安全高效交互。所提出的算法将与人类受试者在与自动驾驶CPS-h（如自动驾驶汽车和四轴飞行器）的互动中进行广泛评估。这项工作将对许多CPS-h领域产生直接影响，包括但不限于多智能体交互、自动驾驶、安全关键场景中人类与自主智能体之间的协作和协调。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 future of cyber-physical systems are smart technologies that can work collaboratively, cooperatively, and safely with humans. Smart technologies and humans will share autonomy, i.e., the right, obligation and ability to share control in order to meet their mutual objectives in the environment of operations. For example, surgical robots must interact with surgeons to increase their capabilities in performing high-precision surgeries, drones need to deliver packages to humans and places, and autonomous cars need to share roads with human-driven cars. In all such interactions, these systems must act safely despite the risks and uncertainties that are intrinsic with humans, technologies, and the environments in which they interact. The key insight of this project is that control strategies can be developed that increase safety in situations where a human needs to closely interact with a cyber-physical system (CPS) that is capable of autonomy or semi-autonomous action.The goal of this project is to develop risk-aware interactive control and planning for achieving safe cyber-physical-human (CPS-h) systems. This project will advance the state-of-the-art of CPS-h planning and control in three main ways: (i) developing computationally tractable risk-aware trajectory planning algorithms that are suited to general autonomous CPS-h, (ii) developing a computationally efficient and empirically supported framework to account for risk-awareness in human’s decision-making, and (iii) deriving interaction-aware planning algorithms for achieving safe and efficient interactions between multiple risk-aware agents. The proposed algorithms will be extensively evaluated with human subjects in interaction with autonomous CPS-h such as autonomous cars and quadcopters. This work will have direct impact on many CPS-h domains including but not limited to multi-agent interactions, autonomous driving, collaboration and coordination between humans and autonomous agents in safety-critical scenarios.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.