A major challenging issue related to the emerging mixed traffic vehicular system, composed of connected and automated vehicles (CAVs) together with human-driven vehicles, is the lack of adequate modeling and control framework, especially at traffic bottlenecks such as highway merging areas. A hierarchical control framework for merging areas is first outlined, where we assume that the merging sequence is decided by a higher control level. The focus of this paper is the lower level of the control framework that establishes a set of control algorithms for cooperative CAV trajectory optimization, defined for different merging scenarios in the presence of mixed traffic. To exploit complete cooperation flexibility of the vehicles, we identify six scenarios, consisting of triplets of vehicles, defined based on the different combinations of CAVs and conventional vehicles. For each triplet, different consecutive movement phases along with corresponding desired distance and speed set-points are designed. Through the movement phases, the CAVs engaged in the triplet cooperate to determine their optimal trajectories aiming at facilitating an efficient merging maneuver, while complying with realistic constraints related to safety and comfort of vehicle occupants. Distinct models are considered for each triplet, and a Model Predictive Control scheme is employed to compute the cooperative optimal control inputs, in terms of acceleration of CAVs, accounting also for human-driven vehicles’ uncertainties, such as drivers’ reaction time and desired speed tracing error. Simulation investigations demonstrate that the proposed cooperative merging algorithms ensure efficient and smooth merging maneuvers while satisfying all the prescribed constraints.

与新兴的混合交通车辆系统有关的主要挑战性问题是由互联和自动车辆（CAV）以及人力驱动的车辆组成，缺乏适当的建模和控制框架，尤其是在高速公路合并区等交通瓶颈时。首先概述了用于合并区域的分层控制框架，在该框架中，我们假定合并顺序由更高的控制级别决定。本文的重点是控制框架的下层，该框架为协作式CAV轨迹优化建立了一套控制算法，针对混合交通的情况下的不同合并场景定义了这些算法。为了充分利用车辆的协作灵活性，我们确定了六种场景，包括三轮车，根据CAV和传统车辆的不同组合定义。对于每个三元组，将设计不同的连续运动阶段以及相应的所需距离和速度设定点。在运动阶段，参与三重态的CAV协作确定其最佳轨迹，旨在促进有效的合并操作，同时遵守与乘员安全和舒适相关的现实约束。针对每个三元组考虑不同的模型，并采用模型预测控制方案来计算协同最优控制输入（就CAV的加速度而言），还应考虑到人类驾驶车辆的不确定性，例如驾驶员的反应时间和期望速度跟踪错误。