  A common approach for modeling the environment of an autonomous vehicle are
dynamic occupancy grid maps, in which the surrounding is divided into cells,
each containing the occupancy and velocity state of its location. Despite the
advantage of modeling arbitrary shaped objects, the used algorithms rely on
hand-designed inverse sensor models and semantic information is missing.
Therefore, we introduce a multi-task recurrent neural network to predict grid
maps providing occupancies, velocity estimates, semantic information and the
driveable area. During training, our network architecture, which is a
combination of convolutional and recurrent layers, processes sequences of raw
lidar data, that is represented as bird's eye view images with several height
channels. The multi-task network is trained in an end-to-end fashion to predict
occupancy grid maps without the usual preprocessing steps consisting of
removing ground points and applying an inverse sensor model. In our
evaluations, we show that our learned inverse sensor model is able to overcome
some limitations of a geometric inverse sensor model in terms of representing
object shapes and modeling freespace. Moreover, we report a better runtime
performance and more accurate semantic predictions for our end-to-end approach,
compared to our network relying on measurement grid maps as input data.
