Adaptions for Automotive Radar Based Occupancy Gridmaps

Environment models are necessary for autonomous driving. The distinction
between drivable and non-drivable underground is elementary. This paper
presents adaptions for radar based occupancy gridmaps, which are a
common representation of the environment. In contrast to standard
occupancy gridmaps or in general standard inverse radar sensor models,
our approach works with velocity dependent parameters and extends free
space calculations. Consequently, the map quality varies less and the
information content of the ego vehicle's immediate vicinity is higher.
Experiments with ground truth data show that the proposed algorithm
produces accurate environment models in urban scenes.