  Grid maps obtained from fused sensory information are nowadays among the most
popular approaches for motion planning for autonomous driving cars. In this
paper, we introduce Deep Grid Net (DGN), a deep learning (DL) system designed
for understanding the context in which an autonomous car is driving. DGN
incorporates a learned driving environment representation based on Occupancy
Grids (OG) obtained from raw Lidar data and constructed on top of the
Dempster-Shafer (DS) theory. The predicted driving context is further used for
switching between different driving strategies implemented within EB robinos,
Elektrobit's Autonomous Driving (AD) software platform. Based on genetic
algorithms (GAs), we also propose a neuroevolutionary approach for learning the
tuning hyperparameters of DGN. The performance of the proposed deep network has
been evaluated against similar competing driving context estimation
classifiers.
