  A path-planning algorithm for connected and non-connected automated road
vehicles on multilane motorways is derived from the opportune formulation of an
optimal control problem. In this framework, the objective function to be
minimized contains appropriate respective terms to reflect: the goals of
vehicle advancement; passenger comfort; and avoidance of collisions with other
vehicles, of road departures and of negative speeds. Connectivity implies that
connected vehicles are able to exchange with each other (V2V) or the
infrastructure (V2I), real-time information about their last generated path.
For the numerical solution of the optimal control problem, an efficient
feasible direction algorithm is used. To ensure high-quality local minima, a
simplified Dynamic Programming algorithm is also conceived to deliver the
initial guess trajectory for the feasible direction algorithm. Thanks to low
computation times, the approach is readily executable within a model predictive
control (MPC) framework. The proposed MPC-based approach is embedded within the
Aimsun microsimulation platform, which enables the evaluation of a plethora of
realistic vehicle driving and advancement scenarios. Results obtained on a
multilane motorway stretch indicate higher efficiency of the optimally
controlled vehicles in driving closer to their desired speed, compared to
ordinary Aimsun vehicles. Increased penetration rates of automated vehicles are
found to increase the efficiency of the overall traffic flow, benefiting manual
vehicles as well. Moreover, connected controlled vehicles appear to be more
efficient compared to the corresponding non-connected controlled vehicles, due
to the improved real-time information and short-term prediction.
