Occupancy grid mapping with cognitive plausibility for autonomous driving applications

This work investigates the validity of an occupancy grid mapping inspired by human cognition and the way humans visually perceive the environment. This query is motivated by the fact that, to date, no autonomous driving system reaches the performance of an ordinary human driver. The mechanisms behind human perception could provide cues on how to improve common techniques employed in autonomous navigation—specifically the use of occupancy grids to represent the environment. We experiment with a neural network that maps an image of the scene onto an occupancy grid representation, and we show how the model benefits from two key (and yet simple) changes: 1) a different format of occupancy grid that resembles the way the brain projects the environment into a warped representation in the cortical visual area; 2) a mechanism similar to human visual attention that filters out non-relevant information from the scene. These effective expedients can potentially be applied to any autonomous driving task requiring an abstract representation of the scenario like the occupancy grids.