An Approach to Shared Control for Automated Driving

ABSTRACT: This seminar presents a solution to decision making for automated driving based on a combination of game theoretic model predictive control (GTPMC) and reinforcement learning (RL). We first present the rationale for shared autonomy (as opposed to fully automated driving) followed by a framework for assignment of control authority to the machine (i.e. vehicle automation) wherein game theory is used to represent the interaction between the human and the machine. This framework is initially evaluated in simple traffic situations where the vehicle is subjected to discretionary and mandatory lane changes. This initial study demonstrates the general feasibility of the proposed approach via a series of simulated case studies. We will subsequently discuss the use of reinforcement learning to capture the interaction of vehicles in more complex traffic situations. The resulting model is then incorporated in the vehicle automation which is then combined with the game theoretic control authority assignment to investigate the proposed shared autonomy framework in relatively complex highway driving settings. Once again, a series of simulated case studies demonstrate the efficacy of the proposed approach while also highlighting the path towards further development of this framework.

BIO: Reza Langari is Professor of Mechanical Engineering and JR Thompson Department Head Chair, Engineering Technology and Industrial Distribution in the College of Engineering at Texas A&M University. Dr. Langari received the B.Sc., M.Sc., and Ph.D. degrees from the University of California, Berkeley, CA, USA, in 1981, 1983, and 1991, respectively. He was with Measurex Corp. (1984-1985); Integrated Systems, Inc. (1985-1986).; and Insight Development Corporation (1987-1989) prior to starting his academic career at Texas A&M University in 1991. He has since held research positions at NASA Ames Research Center, Rockwell International Science Center, United Technologies Research Center, and the U.S. Air Force Research Laboratory. Dr. Langari’s expertise is in the area of computational intelligence, with application to robotics and autonomous systems.  He is the author/co-author of four books and over two hundred technical papers.

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