  Assisted driving for connected cars is one of the main applications that
5G-and-beyond networks shall support. In this work, we propose an assisted
driving system leveraging the synergy between connected vehicles and the edge
of the network infrastructure, in order to envision global traffic policies
that can effectively drive local decisions. Local decisions concern individual
vehicles, e.g., which vehicle should perform a lane-change manoeuvre and when;
global decisions, instead, involve whole traffic flows. Such decisions are made
at different time scales by different entities, which are integrated within an
edge-based architecture and can share information. In particular, we leverage a
queuing-based model and formulate an optimization problem to make global
decisions on traffic flows. To cope with the problem complexity, we then
develop an iterative, linear-time complexity algorithm called Bottleneck
Hunting (BH). We show the performance of our solution using a realistic
simulation framework, integrating a Python engine with ns-3 and SUMO, and
considering two relevant services, namely, lane change assistance and
navigation, in a real-world scenario. Results demonstrate that our solution
leads to a reduction of the vehicles' travel times by 66 in the case of lane
change assistance and by 20 for navigation, compared to traditional,
local-coordination approaches.
