Next generation intelligent transportation systems aim at many cooperative perception and cooperative driving functions that need significant computational resources. Offloading such tasks to some mobile edge computing solutions is considered part of the solution, which is currently investigated in the scope of 5G networks. In the automotive context, such edge systems could be road-side units (RSU), which, however, can easily be overloaded at peak times. Vehicular micro-cloud approaches have been proposed to overcome such problems by sharing computational resources of nearby cars. In this study, we propose an offloading system architecture to enable such offloading such vehicular micro-cloud interconnected by a 5G core network. We model the system as a queueing model to derive closed-form solutions for selected performance metrics. Based on these insights, we propose the Double-Check Offloading Algorithm (DCOA) to obtain the best offloading ratio to the vehicular micro-cloud. Our simulation results show the proposed DCOA has better system performances compared with four other offloading schemes.