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性，即分享控制权的权利、义务和能力，以实现它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，以提高它们执行高精度手术的能力；无人机需要向人类和地点递送包裹；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，这些系统必须安全地运行，尽管人类、技术和它们相互作用的环境存在固有的风险和不确定性。该项目的关键观点是，可以开发控制策略，以提高人类需要与能够自主或半自主操作的网络物理系统(CPS)密切交互的情况下的安全性。该项目的目标是开发具有风险意识的交互控制和规划，以实现安全的网络-物理-人类(CPS-h)系统。该项目将从三个方面推动CPS-h规划和控制的发展：(I)开发适用于一般自主CPS-h的易于计算的风险感知轨迹规划算法，(Ii)开发一个计算高效和经验支持的框架来考虑人类决策中的风险意识，以及(Iii)推导交互感知规划算法，以实现多个风险感知智能体之间的安全和高效交互。建议的算法将在与自动驾驶汽车和四轴飞行器等自主CPS-h交互的人类受试者中进行广泛评估。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、人类与自主智能体在安全关键场景中的协作和协调。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势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.