Human drivers skilled in motorsports disciplines such as circuit racing, drifting, and rally, display an incredible ability to control their vehicle up to the limits of its capability, even in uncertain, changing conditions. Inspired by the possibility of using these capabilities for collision avoidance, autonomous vehicle research has yielded a number of impressive demonstrations of autonomous racing and drifting. However, these systems still do not match the performance of the best human drivers, particularly in terms of robustness to uncertainty. This talk will argue for uncertainty- aware control as a means to help autonomous vehicles behave appropriately cautiously in critical situations and will present our ongoing work in modeling uncertain system dynamics and in using these models for planning and control.

John Subosits received his B.S.E. degree in mechanical and aerospace engineering from Princeton University and M.S. and Ph.D. degrees in mechanical engineering from Stanford University. Currently, he leads the Extreme Performance Intelligent Control group at Toyota Research Institute (TRI). His research interests include algorithms for vehicle control that match the performance, robustness, and adaptability of the best human (racing) drivers.