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Perception of other road users is a crucial task for intelligent vehicles.Perception systems can use on-board sensors only or be in cooperation withother vehicles or with roadside units. In any case, the performance ofperception systems has to be evaluated against ground-truth data, which is aparticularly tedious task and requires numerous manual operations. In thisarticle, we propose a novel semi-automatic method for pseudo ground-truthestimation. The principle consists in carrying out experiments with severalvehicles equipped with LiDAR sensors and with fixed perception systems locatedat the roadside in order to collaboratively build reference dynamic data. Themethod is based on grid mapping and in particular on the elaboration of abackground map that holds relevant information that remains valid during awhole dataset sequence. Data from all agents is converted in time-stampedobservations grids. A data fusion method that manages uncertainties combinesthe background map with observations to produce dynamic reference informationat each instant. Several datasets have been acquired with three experimentalvehicles and a roadside unit. An evaluation of this method is finally providedin comparison to a handmade ground truth.

Collaborative Grid Mapping for Moving Object Tracking Evaluation