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.

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In AAAI 2010 Workshop on Bridging The Gap Between Task And Motion Planning (BTAMP) 2010.

@InProceedings{AAAIWS10-au, title={Motion Planning Algorithms for Autonomous Intersection Management}, author={Tsz-Chiu Au and Peter Stone}, booktitle={AAAI 2010 Workshop on Bridging The Gap Between Task And Motion Planning (BTAMP)}, url="http://www.cs.utexas.edu/users/ai-lab?AAAIWS10-au", year={2010} }