  Modeling and understanding the environment is an essential task for
autonomous driving. In addition to the detection of objects, in complex traffic
scenarios the motion of other road participants is of special interest.
Therefore, we propose to use a recurrent neural network to predict a dynamic
occupancy grid map, which divides the vehicle surrounding in cells, each
containing the occupancy probability and a velocity estimate. During training,
our network is fed with sequences of measurement grid maps, which encode the
lidar measurements of a single time step. Due to the combination of
convolutional and recurrent layers, our approach is capable to use spatial and
temporal information for the robust detection of static and dynamic
environment. In order to apply our approach with measurements from a moving
ego-vehicle, we propose a method for ego-motion compensation that is applicable
in neural network architectures with recurrent layers working on different
resolutions. In our evaluations, we compare our approach with a
state-of-the-art particle-based algorithm on a large publicly available dataset
to demonstrate the improved accuracy of velocity estimates and the more robust
separation of the environment in static and dynamic area. Additionally, we show
that our proposed method for ego-motion compensation leads to comparable
results in scenarios with stationary and with moving ego-vehicle.
