An Intelligent Platooning Algorithm for Sustainable Transportation Systems in Smart Cities

As an important part of a smart city, the Intelligent Transportation System (ITS) is the key to the sustainable development of urban transportation considering the fuel consumption as well as traffic efficiency. Smart sensor can be integrated with the transportation infrastructure to achieve a sustainable ITS. Recent advancements in ITS suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while cooperating with each other to form the sustainable transportation systems. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential to lower fuel consumption and increase traffic efficiency. In this paper, to reach above goals, we present a systematic framework for vehicular platooning forming. Firstly, the optimal speed of a platoon is determined by the optimal speed model, by which the total fuel consumption of this platoon could be saved to a great extend. After that, an optimal insert point decision is made for the vehicles to be grouped in the platoon, based on proposed Q-learning model. We also design a collision detection model to reduce the collision probability when a vehicle intents to join a platoon. Numerical results demonstrate that our proposed model could significantly improve the traffic efficiency, reduce the fuel consumption and decrease the platooning forming time, compared with other classic algorithms.