  The demand for accurate localization has risen in recent years to enable the
emerging of autonomous vehicles. To have these vehicles in the traffic
ecosystem of smart cities, the need for an accurate positioning system is
emphasized. To realize accurate positioning, collaborative localization plays
an important role. This type of localization computes range measurements
between vehicles and improves the accuracy of position by correcting the
possibly faulty values of one of them by using the more accurate values of the
other. 5G signals with the technology of Millimeter Wave (mmWave) support
precise range measurements and 5G networks provide Device to Device (D2D)
communication which improves collaborative localization. The aim of this paper
is to provide an accurate collaborative positioning for autonomous vehicles,
which is less prone to errors utilizing reinforcement learning technique for
selecting the most accurate and suitable range measurement technique for the 5G
signal.
