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A platoon refers to a group of vehicles traveling together in very closeproximity using automated driving technology. Owing to its immense capacity toimprove fuel efficiency, driving safety, and driver comfort, platooningtechnology has garnered substantial attention from the autonomous vehicleresearch community. Although highly advantageous, recent research has uncoveredthat an excessively small intra-platoon gap can impede traffic flow duringhighway on-ramp merging. While existing control-based methods allow foradaptation of the intra-platoon gap to improve traffic flow, making an optimalcontrol decision under the complex dynamics of traffic conditions remains achallenge due to the massive computational complexity. In this paper, wepresent the design, implementation, and evaluation of a novel reinforcementlearning framework that adaptively adjusts the intra-platoon gap of anindividual platoon member to maximize traffic flow in response to dynamicallychanging, complex traffic conditions for highway on-ramp merging. Theframework's state space has been meticulously designed in consultation with thetransportation literature to take into account critical traffic parameters thatbear direct relevance to merging efficiency. An intra-platoon gap decisionmaking method based on the deep deterministic policy gradient algorithm iscreated to incorporate the continuous action space to ensure precise andcontinuous adaptation of the intra-platoon gap. An extensive simulation studydemonstrates the effectiveness of the reinforcement learning-based approach forsignificantly improving traffic flow in various highway on-ramp mergingscenarios.

RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Merging