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