Deriving Spatial Occupancy Evidence from Radar Detection Data

Central low-level sensor data fusion approaches are getting more popular in advanced driver assistant systems. They allow for the resolution of ambiguities in the retrieval of environmental information on the basis of a large, raw data pool. Hereby, one emerging challenge is the unification of sensor data of different formats and sensor types. A popular intermediate layer of data is given by spatial occupancy grids. The conversion of a discrete list of radar detections, which is a commonly utilized measurement format, is problematic due to the sparse spatial resolution. This work addresses this conversion by interpolating the data spatially using generic sensor model knowledge. Traditional approaches derive occupancy evidence in the vicinity of a detection. In addition, we analyze spatial and kinematic properties derived from Doppler measurements, compute likelihoods that multiple detections are caused by the same object and deduce the space between them accordingly. The incorporation of sensor parameters allows full- and short-range radars to be used generically. In addition, we outline the deduction of free space evidence. The elaborated models and algorithms are evaluated on realworld datasets and discussed w.r.t. their applicability in a subsequent Dempster-Shafer-based sensor data fusion approach.