1. Introduction Design automation is one of few area that has been able to build strong ties with niche areas such as  embedded systems  synthetic biology  security These areas will be increasingly important in shaping the future of design automation One direction with tremendous potential left to be discovered: Networks of Autonomous Vehicles (NAV)  heterogeneous computing systems  semiconductor manufacturing Rasit O. Topaloglu, Ph.D (IBM)

2. Problem Statement and Constraints Each vehicle should go from a source to destination  within predefined spatial accuracy limits  minimizing degree and number of accidents  minimizing time and energy required for such transition Competition of limited resources  parking of waiting spot,  passing lane,  one vehicle track Decision time becomes increasingly important in such automation Rasit O. Topaloglu, Ph.D (IBM)

3. Current State of the Art vs. the Future Current research has focused on  Circuits to enable such systems, such as high frequency radar systems  Algorithmic aspects 1 focus on how one vehicle should behave in a sea of vehicles, mostly human-operated  Theoretical aspects 2,3 have not focused on application requirements yet Future  Transition from few autonomous and mostly human-operated regime to mostly/fully autonomous and few/none human-operated regime  Similar to several applications fighting for a fixed set of resources on a computer, bottlenecks and deadlocks may occur  Work targeting known state of the art applications Rasit O. Topaloglu, Ph.D (IBM)

4. DA Expertise to be Used and New Requirements Benefit from DA in designing systems with multi-billion components  Routing algorithms starting point, but less time available to solve and solution quality can be life-critical  Abstractions will exist where a global solution can be a starting point but detailed solutions will follow. Approximations can be used in computation  Partial knowledge of overall system in terms of decisions and timing Models needed for these vehicles  such as top speed and deceleration distance limitations, perhaps also as a function of the road they are on  For drones, maximum height in the sky, proximity allowed to nearby drones, and no-fly-zone restrictions Reliability, security, and privacy considerations  how to deal with faulty or rogue vehicles, avoidance methods, proximity to buildings Rasit O. Topaloglu, Ph.D (IBM)

5. Crosscuts to Other Fields and Benefits Confining NAV to any such field alone would delay progress  computer vision  robotics  circuits  security Impact of an efficient NAV is tremendous  Safe: minimize accidents, live longer  Convenient: speeds up postal and shopping deliveries  Time saver: they drive you while you enjoy or work on the next big problem Design automation can help bring this future soon and we can all be a big part of this vision. Rasit O. Topaloglu, Ph.D (IBM)  real-time operating systems  network communication systems  control systems  sensor networks

6. Acknowledgments and References Thanks to Duane Boning, Prof. (MIT) and Rhett Davis, Prof. (NCSU) for valuable feedback on my initial pitch. 1.J. Levinson and S. Thrun, “Map-based precision vehicle localization in urban environments,” Robotics: Science and Systems (RSS), 2007. 2.F. Koushanfar, A. Davare, D.T. Nguyen, M. Potkonjak, and A. Sangiovanni- Vincentelli, “Low power coordination in wireless ad-hoc networks,” ISLPED, 2003. 3.Y.U. Cao, A.S. Fukunaga, A.B. Kahng, F. Meng, “Cooperative mobile robotics: antecedents and directions,” Intelligent Robots and Systems, 1995. Rasit O. Topaloglu, Ph.D (IBM)