CAREER: Characterizing Attack Resilience of Multi-agent Dynamical Systems with Applications to Connected Autonomous Vehicles

我们的社会目前正在见证新的交通技术的激增，其中包括自动驾驶汽车、自动巴士和无人机等自动驾驶汽车。这些技术承诺了几个好处，如减少道路死亡和拥堵，以及高效的最后一英里运输。然而，实现自动驾驶的竞赛可能会使自动驾驶车辆越来越容易受到传感器、通信和控制信号的攻击。这个职业项目将创建一个集成的研究和教育计划，专注于使包括自动驾驶汽车在内的移动动力系统(或代理)具有弹性的迫切需求，以确保它们即使在传感、通信和控制攻击下也能达到预期目标。有三个核心研究目标。首先基于博弈论设计了一种新颖的建模和分析框架，该框架考虑了单个智能体在对抗性环境中运行时固有的不对称性。第二个是扩展框架，包括多个代理之间的合作和学习，以共同将攻击的影响降至最低。第三是解决在有限机载计算的情况下解决所产生的游戏所产生的可扩展性挑战。拟议的方法将通过使用地面机器人试验台和全尺寸自动驾驶车辆的真实模拟来评估。教育计划包括创建一个基于与攻击韧性相关的创新游戏的活动池，这些游戏将结合工程和艺术，以有效地参与学生活动和人员培训。对公众和执法机构的介绍将有助于形成安全事件根本原因分析和威慑攻击的未来实践。该项目将产生一个整体框架，以研究多智能体动态系统中出现的安全问题的数学基础。与侧重于安全问题具体方面的现有框架相比，这一框架将是一个重大进步。该项目将推进博弈论和计算的多个子领域。首先，它将结合多智能体安全游戏和学习技术，为利用环境不确定性的攻击提供一种新的解决方案。其次，这项工作将建立在随机化算法的进展基础上，以解决多智能体博弈中出现的大型优化问题。该方法将为稳健的多准则优化和多智能体学习提供一种新的概率范式。它将描述系统的攻击弹性和所涉及的计算之间的权衡。第三，分层评估，包括自动驾驶汽车的真实机动性试验台，将促进这一框架向现实世界应用的过渡。这一奖项反映了NSF的法定使命，并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。

Our society is currently witnessing a surge in new transportation technologies that include autonomous vehicles such as self-driving cars, automated buses, and drones. These technologies promise several benefits such as reduced roadway fatalities and congestion, and efficient last-mile delivery. However, the race to achieve autonomy can make autonomous vehicles increasingly susceptible to attacks on their sensors, communications, and control signals. This CAREER project will create an integrated research and education program focusing on the pressing need to make mobile dynamical systems (or agents) including autonomous vehicles resilient – guaranteeing that they meet their intended objectives, even under sensing, communication, and control attacks. There are three core research objectives. The first involves designing a novel modeling and analysis framework based on game theory that factors in the inherent asymmetry when a single agent operates in adversarial environments. The second is to extend the framework to include cooperation and the use of learning between multiple agents to collectively minimize the impact of attacks. The third is to address the scalability challenge that arises in solving the resulting games with limited onboard computation. The proposed methodology will be evaluated through realistic emulations using a ground robotic testbed and scenarios with full-sized autonomous vehicles. The educational plan includes creating a pool of activities based on innovative games related to attack resilience that will combine engineering and the arts for effective student engagement and personnel training. Presentations to the public and law enforcement agencies will help shape future practices for root-cause analyses of security incidents and deter attacks.This project will result in a holistic framework to study the mathematical underpinnings of security problems arising in multi-agent dynamical systems. This framework will be a major advancement compared to existing frameworks that focus on specific aspects of the security problem. The project will advance multiple sub-fields in game theory and computation. First, it will combine multi-agent security games and learning techniques to provide a novel solution to attacks that leverage environmental uncertainty. Second, this work will build on the progress in randomized algorithms to solve large optimization problems arising in multi-agent games. The approach will enable a new probabilistic paradigm for robust multi-criterion optimization and multi-agent learning. It will characterize the tradeoff between the system’s attack resilience and the computation involved. Third, layered evaluations that include a real mobility testbed with autonomous vehicles will facilitate the transition of this framework to real-world applications.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.