  Automotive services for connected vehicles are one of the main fields of
application for new-generation mobile networks as well as for the edge
computing paradigm. In this paper, we investigate a system architecture that
integrates the distributed vehicular network with the network edge, with the
aim to optimize the vehicle travel times. We then present a queue-based system
model that permits the optimization of the vehicle flows, and we show its
applicability to two relevant services, namely, lane change/merge
(representative of cooperative assisted driving) and navigation. Furthermore,
we introduce an efficient algorithm called Bottleneck Hunting (BH), able to
formulate high-quality flow policies in linear time. We assess the performance
of the proposed system architecture and of BH through a comprehensive and
realistic simulation framework, combining ns-3 and SUMO. The results, derived
under real-world scenarios, show that our solution provides much shorter travel
times than when decisions are made by individual vehicles.
