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A Protocol for Mixed Autonomous and Human-Operated Vehicles at Intersections.
Guni
Sharon and Peter Stone.
In Gita Sukthankar and Juan
A. Rodriguez-Aguilar, editors, Autonomous Agents and Multiagent Systems - AAMAS 2017 Workshops, Best Papers, Lecture
Notes in Artificial Intelligence, pp. 151–67, Springer International Publishing, 2017.
[PDF]1.1MB [postscript]7.1MB [slides.pptx]140.9MB
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 (\HIM), 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.
@InCollection{ABMUS17-Sharon,
author = {Guni Sharon and Peter Stone},
title = {A Protocol for Mixed Autonomous and Human-Operated Vehicles at Intersections},
booktitle = {Autonomous Agents and Multiagent Systems - {AAMAS} 2017 Workshops, Best Papers},
Editor={Gita Sukthankar and Juan A. Rodriguez-Aguilar},
Publisher={Springer International Publishing},
year={2017},
pages={151--67},
url = {https://doi.org/10.1007/978-3-319-71682-4_10},
doi = {10.1007/978-3-319-71682-4_10},
timestamp = {Thu, 30 Nov 2017 13:49:41 +0100},
volume = {10642},
series={Lecture Notes in Artificial Intelligence},
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 (\HIM), 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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