Learning Agents for Collaborative Driving

Learning Agents for Collaborative Driving

Charles Desjardins (Laval University, Canada), Julien Laumônier (Laval University, Canada) and Brahim Chaib-draa (Laval University, Canada)
Copyright: © 2009 |Pages: 21
DOI: 10.4018/978-1-60566-226-8.ch011
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Abstract

This chapter studies the use of agent technology in the domain of vehicle control. More specifically, it illustrates how agents can address the problem of collaborative driving. First, the authors briefly survey the related work in the field of intelligent vehicle control and inter-vehicle cooperation that is part of Intelligent Transportation Systems (ITS) research. Next, they detail how these technologies are especially adapted to the integration, for decision-making, of autonomous agents. In particular, they describe an agent-based cooperative architecture that aims at controlling and coordinating vehicles. In this context, the authors show how reinforcement learning can be used for the design of collaborative driving agents, and they explain why this learning approach is well-suited for the resolution of this problem.
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Survey Of Collaborative Driving Systems Based On Agent Technology

This section first surveys what has been done in the field of autonomous vehicle control and collaborative driving systems. Then, it describes how the software agent abstraction and machine learning algorithms have already been used in the design of such systems.

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