  Environment modeling in autonomous driving is realized by two fundamental
approaches, grid-based and feature-based approach. Both methods interpret the
environment differently and show some situation-dependent beneficial
realizations. In order to use the advantages of both methods, a combination
makes sense. This work presents a fusion, which establishes an association
between the representations of environment modeling and then decoupled from
this performs a fusion of the information. Thus, there is no need to adapt the
environment models. The developed fusion generates new hypotheses, which are
closer to reality than a representation alone. This algorithm itself does not
use object model assumptions, in effect this fusion can be applied to different
object hypotheses. In addition, this combination allows the objects to be
tracked over a longer period of time. This is evaluated with a quantitative
evaluation on real sequences in real-time.
