  In current perception systems applied to the rebuilding of the environment
for intelligent vehicles, the part reserved to object association for the
tracking is increasingly significant. This allows firstly to follow the objects
temporal evolution and secondly to increase the reliability of environment
perception. We propose in this communication the development of a multi-objects
association algorithm with ambiguity removal entering into the design of such a
dynamic perception system for intelligent vehicles. This algorithm uses the
belief theory and data modelling with fuzzy mathematics in order to be able to
handle inaccurate as well as uncertain information due to imperfect sensors.
These theories also allow the fusion of numerical as well as symbolic data. We
develop in this article the problem of matching between known and perceived
objects. This makes it possible to update a dynamic environment map for a
vehicle. The belief theory will enable us to quantify the belief in the
association of each perceived object with each known object. Conflicts can
appear in the case of object appearance or disappearance, or in the case of a
confused situation or bad perception. These conflicts are removed or solved
using an assignment algorithm, giving a solution called the " best " and so
ensuring the tracking of some objects present in our environment.
