Research

NSF CRII: Cognizant Learning for Autonomous CPS

The objective of this project is to develop a cognizant learning framework for cyber-physical systems (CPS) that incorporates risk-sensitive and irrational decision making. VIDEO

The necessity for such a framework is exemplified by two observations. First, CPS such as self driving cars will share an environment with other CPS and human users. Human drivers demonstrate a heightened sensitivity to changes in speed and can easily adapt to changes in the environment and road conditions, which makes it essential for a CPS to have an ability to recognize non-rational behaviors. Second, large amounts of data generated during their operation and limited access to models of their environments can make a CPS reliant on machine learning algorithms for decision making in order to meet performance requirements like reachability and safety.

Research efforts in this project will be grounded on improving the behaviors of autonomous vehicles in realistic traffic situations. Outcomes from this project will lead to the development of a research paradigm unifying control, learning, and behavioral economics.

Sponsor: National Science Foundation Computer and Information Science and Engineering Research Initiation Initiative (description)

Role: Sole PI