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Cooperative positioning (CP), which fuses the position-related information of a group of vehicles, is a promising technique to enhance the performance of Global Navigation Satellite Systems (GNSS)-based methods for vehicular positioning in urban environments. With the development of communication techniques, more vehicles can be accessed to build the CP cluster. However, current CP techniques are mainly concentrated on the positioning methods and lack analysis of cooperator selection, and existing node selection methods only consider the ranging information between nodes. In this paper, to build a multi-vehicle CP cluster considering GNSS information, the Cramer-Rao lower bound (CRLB) based on differential GNSS and Ultra-Wide Band (UWB) CP framework is derived, and we demonstrate that the satellite observations have an important impact on CP performance. In order to comprehensively consider the impact of GNSS and UWB on CP cluster, we extend the Greedy search and generalized Breiman, Friedman, Oishen, and Stone (GBFOS) algorithm to select cooperators. Simulation results show that a CP cluster with better performance can be built, compared to only considering the ground ranging.