Dual Inverse Sensor Model for Radar Occupancy Grids

Occupancy grids (OG) are widely used for low-level fusion of radar data in various automotive applications. At the core of OG generation, usually, there is an inverse sensor model (ISM), which is a conditional cell occupancy probability model. Traditional ISM's lack mechanisms decreasing occupancy likelihoods along directions that produce no detections; thus, false detections tend to perpetuate on the OG. In this paper, we propose a novel Inverse Sensor Model including a “positive” component describing occupancy probabilities induced by radar detections and a “negative” component handling lack of detections in a given direction. This dual model proves especially useful in multi-sensor/multi-frame context since false detections by different radars and/or at different moments are uncorrelated and thus can be efficiently mitigated.