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Trustworthy Autonomy: Behavior Prediction and Validation

University of Illinois' Katherine Driggs-Campbell will present Trustworthy Autonomy: Behavior Prediction and Validation at 4 p.m. Nov. 22 at the ITS Transportation Seminar in 290 Hearst Memorial Mining Building.

Abstract

 Autonomous systems, such as self-driving cars, are becoming tangible technologies that will soon impact the human experience. However, the desirable impacts of autonomy are only achievable if the underlying algorithms can handle the unique challenges humans present: People tend to defy expected behaviors and do not conform to many of the standard assumptions made in robotics. To design safe, trustworthy autonomy, we must transform how intelligent systems interact, influence, and predict human agents. My work focuses on human-centered autonomy, using tools from robotics, artificial intelligence, and control to build intelligent systems that safely interact with people.  In this talk, I'll present recent efforts on developing robust autonomy that predicts likely human behaviors and on validating stochastic systems.  We’ll show how these algorithms can be used to improve decision-making and control of a fully equipped test vehicle to operate safely on the road.

Friday, November 22, 2019 - 4:00pm
290 Hearst Memorial Mining Building

Presenter

Katherine Driggs-Campbell

Katie Driggs-Campbell is currently an assistant professor at the University of Illinois at Urbana-Champaign in the Department of Electrical and Computer Engineering. Prior to that, she was a Postdoctoral Research Scholar at the Stanford Intelligent Systems Laboratory in the Aeronautics and Astronautics Department.  She received a B.S.E. with honors from Arizona State University in 2012 and an M.S. from UC Berkeley in 2015. She earned her PhD in 2017 in Electrical Engineering and Computer Sciences from the University of California, Berkeley, advised by Professor Ruzena Bajcsy. Her lab works on human-centered autonomy, focusing on the integration of autonomy into human dominated fields, merging ideas robotics, learning, transportation, and control.