Reinforcement Learning-Driven Relay Selection for Enhanced V2V Communication in Vehicle Platoons

In truck platoons with a bidirectional-leader topology, variations in channel conditions result in unreliability and high latency in vehicle-to-vehicle (V2V) communications. This paper proposes an adaptive relay selection strategy based on Q-learning (QL). The strategy ensures that all vehicles in the platoon receive safety messages from the lead vehicle quickly and reliably. Firstly, relay selection is modeled as a Markov decision process (MDP). The lead vehicle and the relays act as intelligent agents. Agents make decisions adaptively based on real-time state observations in a dynamic communication environment. Secondly, a reward function is designed based on platoon topology and channel state information statistics (CSI). The purpose is to drive the proposed strategy to learn the optimal strategy for message transmission under different environments. Lastly, the simulation results demonstrate the effectiveness and robustness of the proposed algorithm. In various channel attenuation environments, the strategy has been demonstrated to enhance the packet delivery ratio (PDR) for the platoon tail and significantly increase the platoon’s throughput.