A self-enforced, connected cooperative traffic framework

This doctoral thesis proposes a novel approach to road traffic de-conflicting. It comes as a framework consisting of a user-tailored, multi-objective cost function and a negotiation algorithm, in which traffic conflicts are defned within game theoretic formulation, based on side-payment to fairly distribute the benefits, thereby ensuring feasibility within a distributed, intelligent system. The algorithm is then applied to two-agent con-flict resolution in a simulated intersection and platooning/overtake scenarios. Energy consumption and loss of time are compared, indicating a threefold improvement in theoretical efficiency of the framework in relation to a non-cooperative solution. It occurs when agents are the most heterogeneous. The intersection and platooning algorithms are then further developed to handle multi-agent scenarios, where complexity is the greatest challenge. A formulation based on graph theory is proposed, estimating the complexity to be no smaller than that of complete graph sequence, with time of calculation infeasibly long above 10 agents, calling for implementation specifc heuristics. The last chapter of this work considers the framework's future paths of development. It features extended cost function formulations, incorporating, among others, ancillary energy use or battery wear. System's sensitivity cheating or market penetration is also studied, proposing human-in-the-loop architecture as means to ease the adoption process.