  A platoon refers to a group of vehicles traveling together in very close
proximity using automated driving technology. Owing to its immense capacity to
improve fuel efficiency, driving safety, and driver comfort, platooning
technology has garnered substantial attention from the autonomous vehicle
research community. Although highly advantageous, recent research has uncovered
that an excessively small intra-platoon gap can impede traffic flow during
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
challenge due to the massive computational complexity. In this paper, 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
framework's state space has been meticulously designed in consultation with the
transportation literature to take into account critical traffic parameters that
bear direct relevance to merging efficiency. An intra-platoon gap decision
making method based on the deep deterministic policy gradient algorithm is
created to incorporate the continuous action space to ensure precise and
continuous adaptation 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 on-ramp merging
scenarios.
