A Coordinated and Coupled Control and Optimization Method of Intelligent Connected Vehicle Platoons and Traffic Signals Based on Hierarchical Architecture
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摘要: 为克服现有交通控制方法在处理智能网联车辆（intelligent connected vehicles，ICV）与人工驾驶车辆（human-driven vehicles，HDV）混合流时，难以有效协同车辆编队控制与信号配优的局限，研究了1种“车辆-信号”分层协同控制架构，旨在通过下层ICV跟驰控制与上层信号优化的动态联动，实现道路时空资源的一体化高效分配。在下层控制中，为提升编队行驶的稳定性与鲁棒性，对经典智能驾驶人模型（intelligent driver model，IDM）进行了队列式改进，构建了改进的队列式智能驾驶人模型（platoon IDM, PIDM）。引入了1种多前车状态反馈机制，即跟随车的加速度不仅取决于其前车（immediate predecessor）的状态，同时融合了领头车（leader）的速度与间距信息作为前馈补偿项。该机制通过1个可调权重系数k予以实现，有效抑制了由波传播效应引发的编队串扰震荡。通过李雅普诺夫稳定性理论，严格证明了即使在单车发生短时加速/减速故障而偏离平衡状态时，该反馈机制也能确保整个车队系统渐近恢复至稳定行驶平衡点。在上层控制中，设计了1种与下层编队状态动态耦合的信号优化策略。该策略实现了“ICV专用相位”与弹性绿波协调算法的结合：①为ICV车队提供专属通行时间窗；②基于PIDM实时输出的编队平均速度与到达时间预测，动态调整相序与相位差，生成1条穿越多个路口的不停车“绿波带”，从而最小化ICV车队及后续HDV的停车延误。仿真实验表明：本文的PIDM控制模型可在ICV车队发生短时加速或减速故障并使车队运行状态偏离稳定状态时，使其逐渐恢复至原来的平衡状态。当反馈权重系数k的取值范围为[0.075, 0.125]时，PIDM具有较好的控制效果；当响应延误时间T为0 s时，PIDM可以获得理想的控制效果。随着车队模型中响应延误时间T的增加，系统控制量的振幅与频率均增大，但依然能够维持车队系统的稳定运行。此外，在ICV渗透率80%的场景下，协同控制方案较无专用相位方案提升交叉口总通行能力14.16%，ICV专用道容量提升14.78%。研究结果验证了分层架构在保障HDV通行效率的同时，显著提升ICV时空资源利用率的有效性Abstract: To address the challenge of the disconnection between vehicle platoon control and traffic signal optimization in a mixed traffic environment consisting of intelligent connected vehicles (ICV) and human-driven vehicles (HDV), this study investigates a hierarchical "vehicle-signal" cooperative control architecture. The aim is to achieve integrated and efficient allocation of road spatio-temporal resources through dynamic interaction between lower-level ICV car-following control and upper-level signal optimization. In the lower-level control, to enhance platoon stability and robustness, this study improves the classical intelligent driver model (IDM) and constructs an enhanced platoon-based intelligent driver model (PIDM). Its core innovation lies in introducing a multi-predecessor state feedback mechanism, whereby the acceleration of a following vehicle is determined not only by the state of its immediate predecessor but also incorporates the velocity and spacing information of the leader vehicle as a feedforward compensation term. This mechanism is implemented via an adjustable weighting coefficient k, effectively suppressing platoon string instability caused by wave propagation effects. Furthermore, using Lyapunov stability theory, this study rigorously proves that even if a single vehicle experiences short-term acceleration or deceleration failures causing deviation from the equilibrium state, this feedback mechanism ensures the entire platoon system asymptotically returns to a stable operating equilibrium point. In the upper-level control, a signal optimization strategy dynamically coupled with the lower-level platoon states is designed. This strategy pioneers the combination of a "dedicated ICV phase" and an adaptive green wave coordination algorithm: on one hand, it provides exclusive time windows for ICV platoons; on the other hand, based on real-time predictions of platoon average speed and arrival time output by the PIDM, it dynamically adjusts phase sequence and offset to generate a seamless "green wave" band through multiple intersections, thereby minimizing stop delays for both ICV platoons and subsequent HDVs. Simulation experiments demonstrate that the proposed PIDM control model can gradually restore an ICV platoon to its original equilibrium state after short-term acceleration or deceleration failures cause deviation from stability. When the feedback weighting coefficient k falls within the range of [0.075, 0.125], the PIDM achieves satisfactory control performance; ideal control effectiveness is obtained when the response delay time T is 0 s. As the response delay time T increases in the platoon model, the amplitude and frequency of the system control input both increase, yet the platoon system remains stable. Moreover, under an ICV penetration rate of 80%, the cooperative control scheme improves the total intersection throughput by 14.16% and the capacity of the ICV-dedicated lane by 14.78% compared to a scheme without a dedicated phase. The results verify the effectiveness of the hierarchical architecture in significantly enhancing the spatio-temporal resource utilization of ICVs while ensuring the traffic efficiency of HDVs.
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图 2 车辆平衡态速度与车辆间距在不同通信延迟范围内的稳定域变化
Figure 2. Stability domain changes of vehicle equilibrium speed and vehicle distance in different communication delay ranges
图 4 具有ICV专用车道的信号交叉口示意图
Figure 4. Schematic diagram of a signalized intersection with a ICV lane
表 1 各方向交通需求量
Table 1. Traffic demand by direction
单位: 辆/h 车辆类型 出口 进口 东 南 西 北 HDV 东 250 250 南 250 250 西 250 250 北 250 250 ICV 东 1 000 1 000 南 1 000 1 000 西 1 000 1 000 北 1 000 1 000 
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表 2 ICV专用道信号优化控制表现
Table 2. ICV lane signal optimization control performance
评价指标 有ICV专用道信号优化控制 无ICV专用道信号优化控制 增量% 总通行能力 10 000 8 760 14.16 ICV专用道通行能力 8 000 6 970 14.78 HDV车道通行能力 2 000 2 000
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