This project focuses on the formal design of semi-autonomous automotive Cyber Physical Systems (CPS). Rather than disconnecting the driver from the vehicle, the goal is to obtain a vehicle where the degree of autonomy is continuously changed in real-time as a function of certified uncertainty ranges for driver behavior and environment reconstruction. The highly integrated research plan will advance the science and engineering for CPS by developing methods for (1) reconstructing 3D scenes which incorporate high-level topological and low-level metric information, (2) extracting driver behavioral models from large datasets using geometry, reasoning and inferences, (3) designing provably-safe control schemes which trade-off real-time feasibility and conservatism by using the evidence collected during actual driving. Assisting humans in controlling complex and safety-critical systems is a global challenge. In order to improve the safety of human-operated CPS we need to provide guarantees in the reconstruction of the environment where the humans and the CPS operate, and to develop control systems that use predictive cognitive models of the human when interacting with the CPS. A successful and integrated research in both areas will impact not only the automotive sector but many other human-operated systems. These include telesurgery, homeland security, assisted rehabilitation, power networks, environmental monitoring, and all transportation CPS. Graduate, undergraduate and underrepresented engineering students will benefit through classroom instruction, involvement in the research and a continuous interaction with industrial partners who are leaders in the field of assisted driving.

As Cyber-Physical Systems (CPSs) employing mobile nodes continue to integrate into the physical world, ensuring their safety and security become crucial goals. Due to their mobility, real-time, energy and safety constraints, coupled by their reliance on communication mediums that are subject to interference and intentional jamming, the projected complexities in Mobile CPSs will far exceed those of traditional computing systems. Such increase in complexity widens the malicious opportunities for adversaries and with many components interacting together, distinguishing between normal and abnormal behaviors becomes quite challenging. The research work in this project falls along two main thrusts: (1) identifying stealthy attacks and (2) developing defense mechanisms. Along the first thrust, a unifying theoretical framework is developed to uncover attacks in a systematic manner whereby an adversary solves Markovian Decision Processes problems to identify optimal and suboptimal attack policies. The effects of the attacks are assessed through different instantiations of damage and cost metrics. Along the second thrust, novel randomization controllers and randomization-aware anomaly detection mechanisms are developed to prevent, detect and mitigate stealthy attacks. The outcomes of this CAREER project will ultimately provide concrete foundations to build more secure systems in the areas of robotics, autonomous vehicles, and intelligent transportation systems. The educational activities--as in curriculum development and hands-on laboratory experiences--will provide students with the essential skills to build dependable and trustworthy systems, while ensuring the participation of undergraduates, women and underrepresented minorities. The outreach activities will expose high school students to Computer Science education and scientific research.