Abstract: Fully autonomous vehicles are technologically feasible with the current generation of hardware, as demonstrated by recent robot car competitions such as 2007 DARPA Urban Challenge. This milestone creates an opportunity to reconsider modern transportation infrastructure, investigating more efficient systems that leverage a mostly autonomous vehicle population. Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed that intersection control can be made more efficient than traditional control mechanisms, including traffic signals and stop signs. In this talk, I will present a study of the relationship between the precision of cars' motion controllers and the efficiency of the intersection controller. I will then describe a planning-based motion controller that can increase the efficiency of the autonomous intersection control mechanism by reducing the chance that autonomous vehicles stop before intersections. Finally, I will present some more recent developments in my research group, Focusing especially on our mixed reality experiment platform on which a physical vehicle can interact with many simulated vehicles.