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

A platoon refers to a group of vehicles traveling together in very close proximity. It has received significant attention from the autonomous vehicle research community due to its strong potential to significantly enhance fuel efficiency, driving safety, and driver comfort. Despite these advantages, recent research has revealed a detrimental effect of the extremely small intra-platoon gap on traffic flow for highway on-ramp merging. While existing control-based methods allow for adaptation of the intra-platoon gap to improve traffic flow, making an optimal control decision under the complex dynamics of traffic conditions remains a significant challenge due to the massive computational complexity. To this end, we present the design, implementation, and evaluation of a novel reinforcement learning framework that adaptively adjusts the intra-platoon gap of an individual platoon member to maximize traffic flow in response to dynamically changing, complex traffic conditions for highway on-ramp merging. The state space of the framework is carefully designed in consultation with the transportation literature to incorporate critical traffic parameters relevant to merging efficiency. A deep deterministic policy gradient algorithm is adopted to account for the continuous action space to ensure precise and continuous adjustment of the intra-platoon gap. An extensive simulation study demonstrates the effectiveness of the reinforcement learning-based approach for significantly improving traffic flow in various highway merging scenarios.