  The idea of cooperative perception is to benefit from shared perception data
between multiple vehicles and overcome the limitations of on-board sensors on
single vehicle. However, the fusion of multi-vehicle information is still
challenging due to inaccurate localization, limited communication bandwidth and
ambiguous fusion. Past practices simplify the problem by placing a precise GNSS
localization system, manually specify the number of connected vehicles and
determine the fusion strategy. This paper proposes a map-based cooperative
perception framework, named map container, to improve the accuracy and
robustness of cooperative perception, which ultimately overcomes this problem.
The concept 'Map Container' denotes that the map serves as the platform to
transform all information into the map coordinate space automatically and
incorporate different sources of information in a distributed fusion
architecture. In the proposed map container, the GNSS signal and the matching
relationship between sensor feature and map feature are considered to optimize
the estimation of environment states. Evaluation on simulation dataset and
real-vehicle platform result validates the effectiveness of the proposed
method.
