There is a significant push towards greater vehicular autonomy on roads to increase convenience and improve overall driver experience. To enable this autonomy, it is imperative that cyber-physical infrastructure be deployed to enable efficient control and communication. An essential component of such road instrumentation is intersection management. This paper develops an intersection management platform that provides the sensing and communication infrastructure needed to enable efficient intersection management policies. The testbed, located in a indoor laboratory, consists of an intersection and multiple robotic vehicles that can sense and communicate. Whereas traditional approaches to intersection management rely on simulations, this testbed enables the first realistic evaluation of several intersection management policies. Six simple but practical centralized and distributed policies are evaluated and compared against the current state of the art, i.e., traffic signals and stop signs. Through extensive experimentation, this paper concludes that, in the scenario tested, even a simple coordinated management policy can halve vehicular delay, while improving the aggregate traversal time of the intersection by 169%

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In Proceedings of the {ACM/IEEE} Third International Conference on Cyber-Physical Systems (ICCPS 2012), April 2012.

@inproceedings{ICCPS12-Fok, title={A Platform for Evaluating Autonomous Intersection Management Policies}, author={Chien-Liang Fok and Maykel Hanna and Seth Gee and Tsz-Chiu Au and Peter Stone and Christine Julien and Sriram Vishwanath}, booktitle={Proceedings of the {ACM/IEEE} Third International Conference on Cyber-Physical Systems (ICCPS 2012)}, month={April}, url="http://www.cs.utexas.edu/users/ai-lab?ICCPS12-Fok", year={2012} }