Vehicle-infrastructure cooperative control method of connected and signalized intersection in mixed traffic environment
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摘要: 为了提高网联信号交叉口车路协同控制对真实交通环境的适应性，以智能网联汽车与网联人工驾驶汽车混行的典型交通应用场景为研究对象，通过构建八相位网联信号交叉口，研究了混行环境下的交通信号和网联车辆轨迹车路协同优化控制方法；在对场景中的网联车辆运动学特性和跟驰行为进行建模的基础上，构建了一种混行车辆编队方法；基于混行车队模型、安全约束与燃油消耗模型，建立了基于滚动优化的交通信号-车辆轨迹协同优化控制方法；基于异步分层优化思路，将该协同控制问题分解为上层交通信号优化与下层车辆轨迹优化两方面，以交叉口车辆行驶延误时间和燃油消耗量为优化目标，利用遗传算法和“三段式”轨迹优化法分别对交通信号优化问题与车辆轨迹优化问题进行求解；对不同稳态车速与智能网联汽车渗透率下构建的混行交通流的稳定性进行了验证，并通过仿真测试分析了所提出的协同优化控制方法的控制效能与关键参数对控制效能的影响。分析结果表明：在不同交通流量与智能网联汽车渗透率下，提出的控制方法均可有效提升交叉口通行效率与燃油经济性；在完全渗透环境下，较固定配时交通信号控制方法最高可分别提升57.3%和13.3%；随着智能网联汽车渗透率的增加，其控制效能不断提高，较无渗透条件最高可分别提升42.0%和14.2%；即使智能网联汽车渗透率仅达到20%，较无渗透条件也可以在交通效率方面实现20.4%的显著改善；较长的交通信号周期与较短的网联人工驾驶汽车驾驶人反应时间有助于协同控制效能的提升。Abstract: In order to improve the adaption of intelligent vehicle-infrastructure cooperative control methods around connected and signalized intersection to real traffic environment, a novel intelligent vehicle-infrastructure cooperative optimization control method was proposed under the traffic scene of eight-phase connected and signalized intersection mixed intelligent and connected vehicle (ICV) with connected and human-driven vehicle (CHV). Based on modeling the kinematic characteristics and car-following behavior of ICV in the mixed traffic scene, a mixed platoon was formed. A rolling optimization-based cooperative control method of traffic signal and ICV trajectory was proposed based on the platoon model, safety constraints, and fuel consumption model. The cooperative control problem was divided into two layers based on the idea of asynchronous hierarchical optimization, the upper layer was traffic signal timing optimization, and the lower layer was ICV trajectory optimization. Taking the travel time delay and fuel consumption of the vehicle at the intersection as the optimization objectives, the genetic algorithm and three-stage trajectory optimization method were used to solve the traffic signal timing optimization and ICV trajectory optimization, respectively. The stability of the mixed vehicle platoon was verified under different steady-state speeds and penetration rates of ICV. The control effect of the proposed control method and the influence of key parameters on the control effect were analyzed. Analysis results indicate that the proposed control method can effectively improve the traffic efficiency and fuel economy of the intersection under various traffic flows and penetration rates of ICV. In the total ICV environment, the indexes respectively improve by 57.3% and 13.3% when the proposed control method is compared with the method without optimization. Compared with the condition without penetration, with the increase of the penetration rate of ICV, the control efficiency of the proposed control method constantly improves, and the indexes respectively increase by 42.0% and 14.2%. Even if the penetration rate of ICV is only 20%, the proposed control method can also achieve 20.4% improvement in the term of traffic efficiency. The longer traffic signal cycle and the shorter driver reaction time of CHV can provide a benefit for the cooperative control effect. 2 tabs, 13 figs, 40 refs.
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图 8 不同交通流量下的平均旅行时间延误
Figure 8. Average travel time delays under different traffic flows
图 10 不同交通流量和ICV渗透率下平均旅行时间延误
Figure 10. Average travel time delays under different traffic flows and penetration rates of ICV
图 11 不同交通流量和ICV渗透率下平均燃油消耗量
Figure 11. Average fuel consumptions under different traffic flows and penetration rates of ICV
图 12 不同交通信号周期长度对协同控制方法的影响
Figure 12. Influence of different signal cycles on proposed cooperative control method
图 13 不同CHV驾驶人反应时间对协同控制方法的影响
Figure 13. Influence of different drivers' reaction times of CHV on proposed cooperative control method
表 1 仿真参数设置
Table 1. Simulation parameter setting
参数 取值 参数 取值 道路限速/(m·s-1) 11.11 黄灯时长/s 2 车道宽度/m 3.5 仿真步长/s 0.5 最小绿灯时长/s 10 GA种群规模 100 最大绿灯时长/s 40 GA交叉概率 0.8 交通信号周期/s 120 GA变异概率 0.05 交通信号优化间隔/s 30 DBSCAN扫描半径/m 50 车辆轨迹优化间隔/s 0.5 DBSCAN最小包含车辆数/pcu 1 
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表 2 ICV与CHV跟驰模型参数取值
Table 2. Parameter values of ICV and CHV following models
IDM模型参数 取值 CACC模型参数 取值 amax/(m·s-2) 4 kp 0.45 s0/m 2 kd 0.25 TI/s 2 TC/s 0.6 b/(m·s-2) 2 Δt/s 0.01 δ 4 vt0/(m·s-1) 11.11
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