Abstract: Fully-autonomous driving has long been an sought after. DARPA’s efforts dating back a decade has ignited the first spark, showcasing the possibilities. Then, the AI revolution pushed the boundaries. These lead to the creation of a rapidly-growing ecosystem around developing self-driving capabilities. In this talk, we briefly summarize our experience in the DARPA Urban Challenge as Team MIT, one of the six finishers of the race. We then highlight few of our recent research results at MIT, including end-to-end deep learning for parallel autonomy and sparse-to-dense depth estimation for autonomous driving. We conclude with a few questions that may be relevant in the near future.

Bio: Sertac Karaman is an Associate Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (since Fall 2012). He has obtained B.S. degrees in mechanical engineering and in computer engineering from the Istanbul Technical University, Turkey, in 2007; an S.M. degree in mechanical engineering from MIT in 2009; and a Ph.D. degree in electrical engineering and computer science also from MIT in 2012. His research interests lie in the broad areas of robotics and control theory. In particular, he studies the applications of probability theory, stochastic processes, stochastic geometry, formal methods, and optimization for the design and analysis of high-performance cyber-physical systems. The application areas of his research include driverless cars, unmanned aerial vehicles, distributed aerial surveillance systems, air traffic control, certification and verification of control systems software, and many others. He is the recipient of an IEEE Robotics and Automation Society Early Career Award in 2017, an Office of Naval Research (ONR) Young Investigator Award in 2017, Army Research Office (ARO) Young Investigator Award in 2015, National Science Foundation Faculty Career Development (CAREER) Award in 2014, AIAA Wright Brothers Graduate Award in 2012, and an NVIDIA Fellowship in 2011.