Abstract—Fully autonomous vehicles are technologically feasible with the current generation of hardware, as demonstrated by recent robot car competitions. Dresner and Stone proposed a new intersection control protocol called Autonomous Intersection Management (AIM) and showed that with autonomous vehicles it is possible to make intersection control much more efficient than the traditional control mechanisms such as traffic signals and stop signs. The protocol, however, has only been tested in simulation and has not been evaluated with real autonomous vehicles. To realistically test the protocol, we implemented a mixed reality platform on which an autonomous vehicle can interact with multiple virtual vehicles in a simulation at a real intersection in real time. From this platform we validated realistic parameters for our autonomous vehicle to safely traverse an intersection in AIM. We present several techniques to improve efficiency and show that the AIM protocol can still outperform traffic signals and stop signs even if the cars are not as precisely controllable as has been assumed in previous studies. I.

The impressive results of the 2007 DARPA Urban Challenge showed that fully autonomous vehicles are technologically feasible with current intelligent vehicle hardware. It is natural to ask how current transportation infrastructure can be improved when most vehicles are driven autonomously in the future. Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that intersection control can be made more efficient than the traditional control mechanisms such as traffic signals and stop signs. In this paper, we extend the study by examining the relationship between the precision of cars’ motion controllers and the efficiency of the intersection controller. We propose a planning-based motion controller that can reduce the chance that autonomous vehicles stop before intersections, and show that this controller can increase the efficiency of the intersection control mechanism.

The impressive results of the 2007 DARPA Urban Challenge showed that fully autonomous vehicles are technologically feasible with current intelligent vehicle hardware. It is natural to ask how current transportation infrastructure can be improved when most vehicles are driven autonomously in the future. Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that intersection control can be made more efficient than the traditional control mechanisms such as traffic signals and stop signs. In this paper, we extend the study to the real world by examining the relationship between the precision of cars ’ motion controllers and the efficiency of the intersection controller. First, we propose a planningbased motion controller that can reduce the chance that autonomous vehicles stop before intersections. Second, we present a mixed reality simulation environment that allowed an autonomous vehicle in the real world to interact with many virtual vehicles in the AIM simulator. Finally, we experimentally determine a feasible set of parameters for the motion controllers in simulation so as to give a more accurate account of the behavior of autonomous vehicles.

The recent robot car competitions and demonstrations have convincingly shown that fully autonomous vehicles are feasible with current or near-future intelligent vehicle technology. Looking ahead to the time when such autonomous cars will be common, Dresner and Stone proposed a new intersection control protocol called Autonomous Intersection Management (AIM) and showed that by leveraging the capacities of autonomous vehicles we can devise a reservation-based intersection control protocol that is much more efficient than traffic signals and stop signs. Their proposed protocol, however, handles reservation requests one at a time and does not prioritize reservations according to their relative importance and vehicles ’ waiting times, causing potentially large inequalities in granting reservations. For example, at an intersection between a main street and an alley, vehicles from the alley can take a very long time to get reservations to enter the intersection. In this research, we introduce a prioritization scheme to prevent uneven reservation assignments in unbalanced traffic. Our experimental results show that our prioritizing scheme outperforms previous intersection control protocols in unbalanced traffic.