Abstract
Connected and autonomous vehicle technology has advanced rapidly in recent years. These technologies create possibilities for highly efficient, AI-based, transportation systems. One such system is the Autonomous Intersection Management (AIM), an intersection management protocol designed for the time when all vehicles are fully autonomous and connected. Experts, however, anticipate a long transition period during which human and autonomously operated vehicles will coexist. Unfortunately, AIM has been shown to provide little or no improvement over today’s traffic signals when less than 90% of the vehicles are autonomous, making AIM ineffective for a large portion of the transition period. This paper introduces a new protocol denoted Hybrid Autonomous Intersection Management (H-AIM), that is applicable as long as AIM is applicable and the infrastructure is able to sense approaching vehicles. Our experiments show that this protocol can decrease traffic delay for autonomous vehicles even at 1% technology penetration rate.
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Notes
- 1.
This paper assumes driving on the right side of the road. However, the ideas can trivially be generalized to a left side driving policy.
- 2.
Dresner and Stone [5] do not report the turning ratios for their mixed traffic experiment. Our turning ratio was chosen since it results in a good balance between the incoming queues when 100% of the vehicles are HV.
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Acknowledgments
A portion of this work has taken place in the Learning Agents Research Group (LARG) at UT Austin. LARG research is supported in part by NSF (CNS-1330072, CNS-1305287, IIS-1637736, IIS-1651089), ONR (21C184-01), AFOSR (FA9550-14-1-0087), Raytheon, Toyota, AT&T, and Lockheed Martin. The authors would also like to thank the Texas Department of Transportation for supporting this research under project 0-6838, Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas. Peter Stone serves on the Board of Directors of, Cogitai, Inc. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research.
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Sharon, G., Stone, P. (2017). A Protocol for Mixed Autonomous and Human-Operated Vehicles at Intersections. In: Sukthankar, G., Rodriguez-Aguilar, J. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2017. Lecture Notes in Computer Science(), vol 10642. Springer, Cham. https://doi.org/10.1007/978-3-319-71682-4_10
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DOI: https://doi.org/10.1007/978-3-319-71682-4_10
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