University of Michigan

The project is developing novel architectures for control and diagnosis of complex cyber--physical systems subject to stringent performance requirements in terms of safety, resilience, and adaptivity. These ever--increasing demands necessitate the use of formal model--based approaches to synthesize provably--correct feedback controllers.

Complex, networked, distributed cyber-physical systems (CPSs) are emerging in many safety-critical application domains such as aerospace and automotive. Design of such systems heavily relies on insights and experiences of engineers as principled design methodologies that can cope with the complexity of these systems are lacking. As a result, extensive testing and fine-tuning is required to ensure that the final product satisfies the design objectives.

Our research addresses urgent challenges in high confidence testing of automotive systems due to on-going and anticipated introduction of advanced, connected, and autonomous vehicle technologies. We pursue the development of tools for maneuver and data optimization to determine test trajectories and scenarios to facilitate vehicle testing. Our approaches exploit game theoretic traffic interaction modeling to inform in-traffic relevant trajectories, model-free optimization to identify trajectories falsifying time domain specifications, and the development of Smart Black Box