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

Michigan State University, East Lansing MI

Investigators

Abstract

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.