Overview: The goal of this project is to devise computational methods for the optimal coordination of autonomous mobility-on-demand (AMoD) systems, with a central focus on accounting for the couplings between the power and transportation networks. AMoD is a rapidly developing mode of transportation wherein self-driving, electric vehicles transport passengers on demand in a given environment (similar to an Uber system, but with self-driving, electric vehicles).

Cyber-physical systems (CPS), particularly those within the safety-critical domain, must continually ensure that their requirements are being satisfied. To support online requirements satisfaction, a CPS can be modeled with self-adaptive characteristics (SA-CPS) to enable the system to self-reconfigure as uncertainty manifests in both the environment and the system itself.

Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals.

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 have pursued 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 Bla

The proposal targets novel CPS challenges by aiming to develop a system that provides dynamic assessment of the interaction between sea life and smart nets. The CPS explores challenges in machine learning to continuously assess the response to sensory cues for individual species that must be determined in real time to enable modulation of the sensory cues to determine efficacy per species.