Platooning and coordination are two implementation strategies that are frequently proposed for traffic control of connected and autonomous vehicles (CAVs) at signal-free intersections instead of using conventional traffic signals. However, few studies have attempted to integrate both strategies to better facilitate the CAV control at signal-free intersections. To this end, this study proposes a hierarchical control model, named COOR-PLT, to coordinate adaptive CAV platoons at a signal-free intersection based on deep reinforcement learning (DRL). COOR-PLT has a two-layer framework. The first layer uses a centralized control strategy to form adaptive platoons. The optimal size of each platoon is determined by considering multiple objectives (i.e., efficiency, fairness and energy saving). The second layer employs a decentralized control strategy to coordinate multiple platoons passing through the intersection. Each platoon is labeled with coordinated status or independent status, upon which its passing priority is determined. As an efficient DRL algorithm, Deep Q-network (DQN) is adopted to determine platoon sizes and passing priorities respectively in the two layers. The model is validated and examined on the simulator Simulation of Urban Mobility (SUMO). The simulation results demonstrate that the model is able to: (1) achieve satisfactory convergence performances; (2) adaptively determine platoon size in response to varying traffic conditions; and (3) completely avoid deadlocks at the intersection. By comparison with other control methods, the model manifests its superiority of adopting adaptive platooning and DRL-based coordination strategies. Also, the model outperforms several state-of-the-art methods on reducing travel time and fuel consumption in different traffic conditions.

编队行驶和协调是经常提出的两种实施策略，用于在无信号交叉路口对联网和自动驾驶车辆 (CAV) 进行交通控制，而不是使用传统的交通信号。然而，很少有研究试图整合这两种策略以更好地促进无信号交叉口的 CAV 控制。为此，本研究提出了一种名为 COOR-PLT 的分层控制模型，以基于深度强化学习 (DRL) 在无信号交叉口协调自适应 CAV 队列。COOR-PLT 有一个两层框架。第一层使用集中控制策略形成自适应排。每个排的最佳规模是通过考虑多个目标（即效率、公平和节能）来确定的。第二层采用分散控制策略来协调通过十字路口的多个排。每个排都标有协调状态或独立状态，根据这些状态确定其通过的优先级。作为一种高效的 DRL 算法，采用深度 Q 网络 (DQN) 分别确定两层中的排大小和传递优先级。该模型在模拟器上得到验证和检查城市交通模拟(SUMO)。仿真结果表明该模型能够：（1）达到令人满意的收敛性能；(2) 根据不同的交通状况自适应地确定车队规模；(3) 完全避免路口死锁。通过与其他控制方法的比较，该模型体现了其采用自适应队列和基于 DRL 的协调策略的优势。此外，该模型在减少不同交通条件下的旅行时间和燃料消耗方面优于几种最先进的方法。