Challenges and opportunities in autonomy: lessons from self driving cars


We will describe some of the challenges and opportunities in autonomy research today, with a focus on trends and lessons in self-driving research.  We will discuss some of the major challenges and research opportunities in self-driving, including building and maintaining high-resolution maps, interacting with humans both inside and outside of vehicles, dealing with adverse weather, and achieving sufficiently high detection with low probabilities of false alarms in challenging settings.  We will discuss the promise of Deep Learning and the opportunities of developing Parallel Autonomy systems, in which highly automated algorithms operates in parallel with human operators, with the aim of achieving the best of both human and autonomous control.


Dr. John J. Leonard is Samuel C. Collins Professor in the MIT Department of Mechanical Engineering and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).  His research addresses the problems of navigation and mapping for autonomous mobile robots and underwater vehicles.  He holds the degrees of B.S.E.E. in Electrical Engineering and Science from the University of Pennsylvania (1987) and D.Phil. in Engineering Science from the University of Oxford (1994).  He was team leader for MIT's DARPA Urban Challenge team, which was one of eleven teams to qualify for the Urban Challenge final event and one of six teams to complete the race.  He is the recipient of an NSF Career Award (1998) and the King-Sun Fu Memorial Best Transactions on Robotics Paper Award (2006).  He is an IEEE Fellow (2014).  Since 2016, Professor Leonard has also served as Vice President of Automated Driving Research at the Toyota Research Institute (TRI). At TRI, Dr. Leonard is helping to lead an effort to create the Toyota Guardian system for increasing the safety of human driving by exploiting advanced perception and navigation capabilities developed by the mobile robotics research community.