Optimization-based path-planning for connected and non-connected automated vehiclesOptimization-based path-planning for connected and non-connected automated vehicles Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2022-07-132022enA 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 and of road departures. Connectivity implies, within the present work, that connected vehicles can exchange with each other (V2V) real-time information about their last generated short-term path. For the numerical solution of the optimal control problem, an efficient feasible direction algorithm (FDA) is used. To ensure high-quality local minima, a simplified Dynamic Programming (DP) algorithm is also conceived to deliver the initial guess trajectory for the start of the FDA iterations. Thanks to very 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 under different vehicles mixes. 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 manually driven 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 in achieving their desired speed, compared also to the corresponding non-connected controlled vehicles, due to the improved real-time information and short-term prediction achieved via V2V communication.The research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation programme/ ERC Grant Agreement n. [833915], project TrafficFluid. http://creativecommons.org/licenses/by-nc-nd/4.0/Transportation Research Part C: Emerging Technologies134103487Typaldos_et_al._Transp. Res. Part C Emerging Technol._134_2022_preprint.pdfChania [Greece]Library of TUC2022-07-08application/pdf5.7 MBfree Typaldos Panagiotis Τυπαλδος Παναγιωτης Papageorgiou Markos Παπαγεωργιου Μαρκος Papamichail Ioannis Παπαμιχαηλ Ιωαννης Elsevier Path planning Model predictive control Automated vehicles Vehicle trajectory optimization Connected vehicles