Enabling technologies of connected and automated vehicles (CAVs) bring new opportunities to signalized intersection control. CAVs not only provide a new source of data for traffic management but also can be controlled as actuators to improve traffic flow. This study proposes a hierarchical and implementation-ready cooperative driving framework with a mixed traffic composition of CAVs, connected vehicles (CVs), and regular vehicles (RVs) for urban arterials. The proposed framework combines centralized and distributed control concepts, where the infrastructure generates optimal signal timing plans and provides high-level trajectory guidance to the CAVs while detailed trajectories are generated by each vehicle. The system consists of three levels of models. At the vehicle level, a state transition diagram is designed for different modes of operations of CAVs including eco-trajectory planning, cooperative adaptive cruise control (CACC) and collision avoidance. At the intersection level, a mixed-integer linear programming (MILP) problem is formulated to optimize the signal timing plan and arrival time of CAVs, with consideration of CACC platooning behaviors. At the corridor level, link performance functions are applied to calculate the total delay of the coordinated phases of each intersection, and a linear programming (LP) problem is formulated to optimize the offsets for every cycle, which are then passed to the intersection level. Simulation results from a calibrated real-world arterial corridor show that both mobility and fuel economy benefits from the cooperative driving framework. The total delay is reduced by 2.2%−33.0% and fuel consumption by 3.9%−7.4%, with different mixture of vehicle compositions and CAV penetration rates (e.g., 0%−100%). Sensitivity analysis on volume fluctuation is performed, which confirms the benefits of the dynamic offset optimization.

联网和自动驾驶汽车（CAV）的使能技术为信号交叉口控制带来了新机遇。CAV不仅为交通管理提供了新的数据源，而且还可以作为促动器进行控制以改善交通流量。这项研究提出了一种分层的，可实施的合作驾驶框架，其中包括用于城市干线的CAV，联网车辆（CV）和常规车辆（RV）的混合交通组成。所提出的框架结合了集中式和分布式控制概念，其中基础架构生成最佳信号时序计划，并为CAV提供高级轨迹指导，而每辆车均会生成详细轨迹。该系统包括三个级别的模型。在车辆层面，针对CAV的不同操作模式设计了状态转换图，包括生态轨迹规划，协作式自适应巡航控制（CACC）和避免碰撞。在交叉点级别，考虑了CACC排行行为，提出了混合整数线性规划（MILP）问题，以优化CAV的信号定时计划和到达时间。在廊道级别，应用链接性能函数来计算每个交叉路口协调相位的总延迟，并制定线性规划（LP）问题以优化每个周期的偏移量，然后将其传递到交叉路口级别。经过校准的真实世界的动脉走廊的仿真结果表明，机动性和燃油经济性都受益于协作驾驶框架。总延迟减少了2。2％-33.0％，燃油消耗量为3.9％-7.4％，车辆组成和CAV渗透率的混合比例不同（例如0％-100％）。进行了对体积波动的敏感性分析，这证实了动态偏移优化的好处。