This paper proposes an innovative distributed longitudinal control strategy for connected automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven vehicles (HDVs), incorporating high-dimensional platoon information. For mixed traffic, the traditional CAV control method focuses on microscopic trajectory information, which may not be efficient in handling the HDV stochasticity (e.g., long reaction time; various driving styles) and mixed traffic heterogeneities. Different from traditional methods, our method, for the first time, characterizes consecutive HDVs as a whole (i.e., AHDV) to reduce the HDV stochasticity and utilize its macroscopic features to control the following CAVs. The new control strategy takes advantage of platoon information to anticipate the disturbances and traffic features induced downstream under mixed traffic scenarios and greatly outperforms the traditional methods. In particular, the control algorithm is based on deep reinforcement learning (DRL) to fulfill car-following control efficiency and further address the stochasticity for the aggregated car following behavior by embedding it in the training environment. To better utilize the macroscopic traffic features, a general platoon of mixed traffic is categorized as a CAV-HDVs-CAV pattern and described by corresponding DRL states. The macroscopic traffic flow properties are built upon the Newell car-following model to capture the characteristics of aggregated HDVs' joint behaviors. Simulated experiments are conducted to validate our proposed strategy. The results demonstrate that the proposed control method has outstanding performances in terms of oscillation dampening, eco-driving, and generalization capability.

本文提出了一种创新的分布式纵向控制策略，用于连接自动驾驶车辆（CAV）在 CAV 和人类驾驶车辆（HDV）的混合交通环境中，结合高维队列信息。对于混合交通，传统的CAV控制方法侧重于微观轨迹信息，这在处理HDV随机性（例如，长反应时间；各种驾驶风格）和混合交通异质性方面可能效率不高。与传统方法不同，我们的方法首次将连续的 HDV 表征为一个整体（即 AHDV），以降低 HDV 的随机性并利用其宏观特征来控制后续的 CAV。新的控制策略利用排信息来预测混合交通场景下下游引起的干扰和交通特征，并且大大优于传统方法。特别是，该控制算法基于深度强化学习 (DRL)，以实现跟车控制效率，并通过将其嵌入训练环境进一步解决聚合跟车行为的随机性问题。为了更好地利用宏观交通特征，混合交通的一般排被归类为 CAV-HDVs-CAV 模式，并由相应的 DRL 状态描述。宏观交通流属性建立在 Newell 跟车模型之上，以捕捉聚合 HDV 联合行为的特征。进行了模拟实验以验证我们提出的策略。结果表明，所提出的控制方法在减振、生态驾驶和泛化能力方面具有突出的性能。