  The comprehensive representation and understanding of the driving environment
is crucial to improve the safety and reliability of autonomous vehicles. In
this paper, we present a new approach to establish an environment model
containing a segmentation between static and dynamic background and parametric
modeled objects with shape, position and orientation. Multiple laser scanners
are fused into a dynamic occupancy grid map resulting in a 360{\deg} perception
of the environment. A single-stage deep convolutional neural network is
combined with a recurrent neural network, which takes a time series of the
occupancy grid map as input and tracks cell states and its corresponding object
hypotheses. The labels for training are created unsupervised with an automatic
label generation algorithm.
  The proposed methods are evaluated in real-world experiments in complex inner
city scenarios using the aforementioned 360{\deg} laser perception. The results
show a better object detection accuracy in comparison with our old approach as
well as an AUC score of 0.946 for the dynamic and static segmentation.
Furthermore, we gain an improved detection for occluded objects and a more
consistent size estimation due to the usage of time series as input and the
memory about previous states introduced by the recurrent neural network.
