Reputation-based User Vehicle Assignment in Intelligent and Connected Vehicle Platoons

Abstract

Vehicle platooning is a promising and emerging framework in intelligent transportation systems. Recent works consider reputation-based approaches for head selection in single platoons, in order to optimize safety and security. When a large number of user vehicles having the same destination want to benefit from platooning, several platoons need to be formed. To this end, user vehicles are allocated to different platoons, with each platoon being led by a platoon head. To ensure necessary network bandwidth and latency, the number of user vehicles between the newly formed platoons must be balanced. However, an arbitrary assignment of user vehicles in platoons can bias the future selection of the platoon heads in such reputation-based approaches. This work considers reputation-based platooning systems and proposes an optimal approach to balance the number of user vehicles in platoons while at the same time ensures fairness in the reputation score of platoon heads. A mixed-linear integer programming formulation is proposed, which provides an optimal allocation of the user vehicles in platoons based on the above objectives. The approach is validated using multiple synthetically generated datasets using 14 different input parameters. The obtained results demonstrate the optimality of the proposed method while achieving significant time performance speedups (∼140x on average) when compared to a brute-force exhaustive method.