Collaborative driving is an important subcomponent of Intelligent ltausportation Systems ITS as it strives to create autonomous vehicles that are able to cooperate in order to navigate through urban traffic by using communications. In this paper, we address this pmblematic using a platoon of cars considered as a multiagent system. To do that, we propose a hierarchical architecture based on three layers (guidance, management, traffic control) which can be used to develop centralized platoons (where a head vehicle-agent coordinates other ' vehicle-agents by applying coordination rules) and decentralized platoons (where the platoon is considered as a team of vehicle-agents maintaining the platoon together). We propose the model of teamwork used in multiagent systems as a decentralized alternative to previous coordination centralized on the platoon's leader and outline its benefits using collaborative driving simulation scenarios.
Intelligent agents are applied to automate the process of negotiation. Typical multi-agent negotiation strategies only allow the exchange of precise quantitative values as attributes. In the real-world applications, many of the negotiation issues such as qualitative concepts, cannot be expressed by exact numeric values.In this paper, we propose a new fuzzy-based model for negotiation. Our model leads to a human-like negotiation, and enables negotiation parties to act flexibly.
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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