Real Time Driving Context Understanding Using Deep Grid Net A Granular Approach

Numerous self-driving cars algorithms rely on grid maps for motion planning, obstacles avoidance or environment perception. Obtained from fused sensory information, the occupancy grids (OGs) are nowadays among the most popular solutions used in series production in automotive industry. In this paper, we extend Deep Grid Net (DGN), a deep learning(DL) system designed for understanding the context in which an autonomous car is driving.We consider this paper a granular approach to DGN method due to the improvements added to the original research.