![](bg4.png)
in a centralized manner. To avoid deadlocks, all the
decisions must be taken with anticipation, allowing
the forecast of possible deadlocks and its resolution
before they actually occur.
Interface Manager (IM) agent deals with the selec-
tion of the most appropriate message interface to the
driver, taken in account the type of message.
Localization agent determines the localization of the
vehicle in the intersection map, using GPS data and
an intersection beam signal, and compares its posi-
tion with neighbor vehicles positions, periodically
transmitted through wireless communications. This
agent must decide whether the situation is critical,
based on position and vehicle data, and warn UM in
case of imminent danger.
Vehicle agent gathers vehicle data (e.g. speed, ac-
celeration, brakes, steering) and feeds Localization
agent with that information. UM receives also simi-
lar feedback. Moreover, this agent gets commands
issued by Driver agent.
Driver agent deals with the control of the whole ve-
hicle. It receives information, whether critical or not,
via IM agent and responds accordingly to that in-
formation and the type of driver modeled. For that
purpose, Driver agent maintains a driver type data-
base. This agent issues commands to Vehicle agent
directly and indirectly through IM.
Traffic Simulation Environment represents the en-
vironment where the agents evolve. One of its main
functions is to provide communications between
agents, in the platform level, allowing appropriate
management of agents’ percepts and actions.
Graphical presentation of simulation results will also
be directly connected with this component.
5 CONCLUSIONS AND FUTURE
WORK
In this paper we propose an architecture in which the
simulation and management of the inter-vehicle
communications are integrated in the simulation of
vehicles, in a hierarchical multi-agent environment.
We also present a short survey of existing method-
ologies, platforms, ontologies and languages, and
suggest some possible choices to allow appropriate
system implementation.
MAS development using the appropriate meth-
odology, the implementation of the solution in the
selected platform, the validation of the process and
final deployment will follow.
ACKNOWLEDGEMENTS
This work was supported by Institute of Systems and
Robotics and Fundação para a Ciência e Tecnologia
under contract NCT04:POSC/EEA/SRI/
58016/2004.
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