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Exploring the Potential of Multiagent Learning for Autonomous Intersection Control

Exploring the Potential of Multiagent Learning for Autonomous Intersection Control

Matteo Vasirani, Sascha Ossowski
Copyright: © 2009 |Pages: 11
ISBN13: 9781605662268|ISBN10: 1605662267|ISBN13 Softcover: 9781616924720|EISBN13: 9781605662275
DOI: 10.4018/978-1-60566-226-8.ch013
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MLA

Vasirani, Matteo, and Sascha Ossowski. "Exploring the Potential of Multiagent Learning for Autonomous Intersection Control." Multi-Agent Systems for Traffic and Transportation Engineering, edited by Ana Bazzan and Franziska Klügl, IGI Global, 2009, pp. 280-290. https://doi.org/10.4018/978-1-60566-226-8.ch013

APA

Vasirani, M. & Ossowski, S. (2009). Exploring the Potential of Multiagent Learning for Autonomous Intersection Control. In A. Bazzan & F. Klügl (Eds.), Multi-Agent Systems for Traffic and Transportation Engineering (pp. 280-290). IGI Global. https://doi.org/10.4018/978-1-60566-226-8.ch013

Chicago

Vasirani, Matteo, and Sascha Ossowski. "Exploring the Potential of Multiagent Learning for Autonomous Intersection Control." In Multi-Agent Systems for Traffic and Transportation Engineering, edited by Ana Bazzan and Franziska Klügl, 280-290. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-226-8.ch013

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Abstract

The problem of advanced intersection control is being discovered as a promising application field for multiagent technology. In this context, drivers interact autonomously with a coordination facility that controls the traffic flow through an intersection, with the aim of avoiding collisions and minimizing delays. This is particularly interesting in the case of autonomous vehicles that are controlled entirely by agents, a scenario that will become possible in the near future. In this chapter, the authors seize the opportunities of multiagent learning offered by such a scenario, by introducing a coordination mechanism where teams of agents coordinate their velocities when approaching the intersection in a decentralized way. They show that this approach enables the agents to improve the intersection efficiency, by reducing the average travel time and so contributing to alleviate traffic congestions.

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