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Grid maps obtained from fused sensory information are nowadays among the mostpopular approaches for motion planning for autonomous driving cars. In thispaper, we introduce Deep Grid Net (DGN), a deep learning (DL) system designedfor understanding the context in which an autonomous car is driving. DGNincorporates a learned driving environment representation based on OccupancyGrids (OG) obtained from raw Lidar data and constructed on top of theDempster-Shafer (DS) theory. The predicted driving context is further used forswitching between different driving strategies implemented within EB robinos,Elektrobit's Autonomous Driving (AD) software platform. Based on geneticalgorithms (GAs), we also propose a neuroevolutionary approach for learning thetuning hyperparameters of DGN. The performance of the proposed deep network hasbeen evaluated against similar competing driving context estimationclassifiers.

Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding