CAREER: Safe and Agile Autonomous Cyber-Physical Systems

自主网络物理系统（CPS），如自动驾驶汽车和无人机，由深度学习和基于人工智能的感知、规划和控制算法提供动力，正在构成我们国家重要基础设施的基础，并提出了直接和紧迫的安全关键挑战。目前部署自主CPS方法的一个主要限制是确保系统在正常操作条件下不经常发生的情况下安全可靠地运行，因此难以收集数据。例如，一辆经过训练遵循“道路规则”的自动驾驶汽车在大多数情况下都会表现良好，但只有在不寻常的情况下，也就是边缘情况下，才会构成最严峻的安全挑战。这个项目提出了一个创新的想法——增加自动驾驶汽车的敏捷性能提高其安全性吗？这个概念有些争议，因为敏捷性（如赛车）更经常与安全边际的降低联系在一起。在这些挑战的推动下，自动驾驶汽车的实际测试和安全性面临着挑战，该项目的目标是在敏捷性、安全性及其相互作用这两个方面为自动驾驶网络物理系统奠定基础。该项目的核心是(1)通过开发敏捷运动规划的新方法来提高自动驾驶汽车的敏捷性，这样他们就可以在最重要的时候避开潜在的不安全状况时在处理和控制的极限下进行机动；(2)自动推理自主CPS操作过程中可能发生的不确定动态情况；(3)开发在设计时自动生成测试和边缘案例场景的新方法。探索自主CPS失败的情况。所提出的方法将在自动驾驶汽车测试平台、逼真和高保真仿真平台以及全尺寸自动驾驶汽车原型上进行评估。该项目不仅会考虑无人驾驶汽车的安全性，还会考虑可能有乘客在场的汽车的安全性。这个CAREER项目包括设计令人兴奋的新课程，以及围绕自主赛车的倡议，以参与K-12、本科生和研究生的研究和指导。该项目旨在通过在理论、应用和实践平台开发之间建立更强的联系，确保学生培养对网络物理系统和自治系统的整体看法。该项目将有助于增强自主网络物理系统的能力，并促进其安全部署。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。

Autonomous Cyber-Physical Systems (CPS), such as self-driving cars, and drones, powered by deep learning and AI based perception, planning, and control algorithms, are forming the basis for significant pieces of our nation’s critical infrastructure, and present direct, and urgent safety-critical challenges. A major limitation with current approaches towards deploying autonomous CPS is in ensuring that the system operates safely, and reliably in situations that do not happen very often under normal operating conditions and are therefore difficult to gather data on. For instance, a self-driving car trained to follow the ‘rules of the road’ will perform well most of the time, but it is the unusual conditions, the edge cases, which pose the hardest safety challenges. This project brings forward an innovative idea – can increasing the agility of an autonomous vehicle improve its safety? This notion is somewhat controversial since agility (like that of race cars) is more frequently associated with decreased safety margins.Motivated by these challenges underlying real-world testing and safety for autonomous vehicles, the goal of this project is to develop the foundations for autonomous cyber-physical systems along two dimensions: agility, safety, and their interplay. The project is centered on (1) increasing agility for AVs by developing new methods for agile motion planning, so they can maneuver at the limits of their handling and control when it matters most to escape potentially unsafe conditions, (2) automated reasoning about uncertain dynamic situations that may occur during autonomous CPS operation, and (3) developing novel methods for automatically generating testing and edge-case scenarios at design time, to explore scenarios under which the autonomous CPS would fail. The proposed methods will be evaluated on scaled autonomous vehicles testbeds, on photorealistic and high-fidelity simulation platforms, and on full scale AV prototypes. The project will also consider not just safety of an unoccupied AV – but one in which passengers may be present. This CAREER project includes designing exciting new courses, and initiatives centered around autonomous racing to engage with research and mentoring for K-12, undergraduate, and graduate students. The project aims to ensure that students cultivate a holistic view of cyber-physical systems and autonomous systems by drawing stronger connections between theory, applications, and hands-on platform development. The project will help enhance the capabilities of autonomous cyber-physical systems and facilitate with their safe deployment.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.