Heterogeneous Platooning Decision Making Algorithm based on Optimization and Deep Learning

Cooperative driving is one of the important research topics in the transportation field due to the positive impact on road capacity and safety as well as fuel consumption. One of the most well-known applications of cooperative driving is vehicle platooning, which has proven its ability to improve the fuel consumption of its members. However, the fuel consumption of a vehicle during a highway journey depends on many other factors; such as destinations and speeds of the solo vehicles as well as the platoon members. Hence, a joining decision-making algorithm is proposed in this paper to estimate the potential fuel savings from joining the candidate platoons on a highway based on optimizing the joining speed profile using Sequential Quadratic Programming as well as Genetic Algorithm. Six different scenarios tested the performance of the decision-maker in terms of fuel and time savings as well as the computational cost. Furthermore, a deep neural network model is proposed to try to maintain the optimizer accuracy and reduce the computational cost, which was successfully obtained from the testing experiments.