Towards Self-Organizing connected and autonomous Vehicles: A coalitional game theory approach for cooperative Lane-Changing decisions

Abstract

This research introduces a novel approach to cooperative decision-making among self-organizing connected and autonomous vehicles (CAVs). In this approach, a coalitional game is played by a group of players who form alliances of different sizes based on the collective payoff they receive. The players continuously evaluate the potential benefits of different coalition formations and adjust their decisions accordingly. The proposed approach utilizes the V2V communication feature of CAVs, which enables CAVs to participate in a cooperative game, thereby resolving conflicting situations that often arise during lane-changing decisions. By working together within the same coalition, CAVs on a hypothetical three-lane freeway segment can collectively determine their target lanes, rather than engaging in individual decision-making that could result in a win-lose situation. The proposed approach considers up to nine CAVs interacting with each other and aims to find Pareto-optimal coalitions in lane-changing decisions. The approach considers lead CAVs that cooperate via acceleration to enlarge the gap between the subject and lead CAVs. The game is modelled as a dynamic transferable utility problem, allowing the utilities obtained from the coalition agreement to be expressed as real numbers and distributed among coalition members. The framework is generalizable to other traffic and demand management problems while the cooperative CAVs can be compensated for reaching an agreement in a universal, collectible, and tradable credit scheme (UCTCS) that can be used in a wide spectrum of traffic and demand management applications. The effects of the proposed coalitional lane-changing decision-making on traffic efficiency are compared to a non-cooperative decision-making model on a simulated road segment. Overall, our analysis suggests that the proposed coalitional approach can positively impact macroscopic traffic characteristics, leading to potentially improved traffic flow, reduced congestion, and enhanced travel time efficiency.