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，可以与人类协作，合作和安全地工作。智能技术和人类将共享自主权，即，分享控制权的权利、义务和能力，以实现其在业务环境中的共同目标。例如，手术机器人必须与外科医生互动，以提高他们执行高精度手术的能力，无人机需要将包裹运送到人类和地方，自动驾驶汽车需要与人类驾驶的汽车共享道路。在所有这些交互中，这些系统必须安全地运行，尽管存在人类、技术和它们相互作用的环境所固有的风险和不确定性。该项目的关键见解是，控制策略可以开发，以提高安全的情况下，人类需要密切互动的网络物理系统（CPS），能够自主或半自主action.The项目的目标是开发风险意识的交互控制和规划，实现安全的网络物理人（CPS-h）系统。该项目将通过三个主要方式推进CPS-h规划和控制的最新水平：（i）开发适合于一般自主CPS-h的计算上易处理的风险感知轨迹规划算法，（ii）开发计算上高效且经验支持的框架以考虑人类决策中的风险感知，以及（iii）导出用于实现多个风险感知代理之间的安全和有效交互的交互感知规划算法。所提出的算法将与人类受试者在与自主CPS-h（如自主汽车和四轴飞行器）的交互中进行广泛评估。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和自动智能体之间的协作和协调。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。

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.