Centralized Cooperative Control of Mixed Vehicle Groups Considering Multi-vehicle Influence under Cloud-edge-end Collaboration
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摘要: 随着车联网技术的进步, 由网联人驾车与网联自动车组成的混行车群规模正逐渐增大, 导致混行车群间的协同与交互难度增加, 进而影响混行车群行驶状态的一致性. 为解决此问题, 提出一种云−边−端协同下考虑多车影响的混行车群集中式协同控制方法, 以提高混行车群协同行驶效率. 首先, 为有效处理和分析较大规模混行车群产生的海量异构信息数据, 设计混合交通场景下云−边−端协同架构. 然后, 考虑网联人驾车前方两辆车及紧邻后车状态信息的影响, 以及网联自动车前方所有车辆及紧邻后车状态信息的影响, 分别在云控平台建立基于分子动力学的网联自动车和固定权重的网联人驾车协同行驶模型. 再者, 根据混行车群间动态信息影响关系, 设计基于云−边−端协同架构的混行车群集中式协同控制方法, 并利用稳定性和串稳定性理论获得混行车群协同行驶一致性条件. 最后, 通过对比仿真实验验证了本文所提控制方法的有效性.Abstract: With the advancement of vehicular networking technology, the scale of mixed vehicle groups composed of connected human-driving vehicles (CHVs) and connected automated vehicles (CAVs) is gradually increasing, which leads to increased difficulty in cooperation and interaction between mixed vehicle groups, thereby affecting the consistency of the driving state of mixed vehicle groups. In response to this issue, a centralized cooperation control method of mixed vehicle groups considering multi-vehicle influence under cloud-edge-end collaboration is proposed, so as to improve the efficiency of cooperative driving of mixed vehicle groups. First, to effectively process and analyze the massive heterogeneous information data generated by large-scale mixed vehicle groups, a cloud-edge-end collaborative architecture is designed for mixed traffic scenarios. Then, considering the influence of the state information of the two vehicles in front of the CHVs and the immediate following vehicle, as well as the state information of all vehicles in front of the CAVs and the immediate following vehicle, the cooperative driving models for CAVs based on molecular dynamics and for CHVs based on fixed weight are established on the cloud control platform, respectively. Furthermore, considering the dynamic information influence relationships between mixed vehicle groups, a centralized cooperative control method for mixed vehicle groups is designed based on the cloud-edge-end collaborative architecture, and the consistency conditions for cooperative driving of mixed vehicle groups are obtained by using stability and string stability theory. Finally, the comparative simulation experiments are conducted to verify the effectiveness of the proposed control method in this paper.
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图 3 云−边−端协同下考虑多车影响的混行车群集中式协同控制机理
Fig. 3 Centralized cooperative control mechanism for mixed vehicle groups considering multi-vehicle influence under cloud-edge-end collaboration
图 4 无通信时延下混行车群采用FVD模型时车辆的状态变化曲线
Fig. 4 State variation curves of vehicles within the mixed vehicle groups using FVD model without communication delay
图 5 无通信时延下混行车群采用FVD模型时跟随车辆与前车的状态差值变化曲线
Fig. 5 State error variation curves between following vehicles and preceding vehicles within the mixed vehicle groups using FVD model without communication delay
图 6 无通信时延下混行车群采用IDM模型时车辆的状态变化曲线
Fig. 6 State variation curves of vehicles within the mixed vehicle groups using IDM model without communication delay
图 7 无通信时延下混行车群采用IDM模型时跟随车辆与前车的状态差值变化曲线
Fig. 7 State error variation curves between following vehicles and preceding vehicles within the mixed vehicle groups using IDM model without communication delay
图 8 无通信时延下混行车群采用MFRVI-CDM模型时车辆的状态变化曲线
Fig. 8 State variation curves of vehicles within the mixed vehicle groups using MFRVI-CDM model without communication delay
图 9 无通信时延下混行车群采用MFRVI-CDM模型时跟随车辆与前车的状态差值变化曲线
Fig. 9 State error variation curves between following vehicles and preceding vehicles within the mixed vehicle groups using MFRVI-CDM model without communication delay
图 10 有通信时延下混行车群采用MFRVI-CDM模型时车辆的状态变化曲线
Fig. 10 State variation curves of vehicles within the mixed vehicle groups using MFRVI-CDM model with communication delay
图 11 有通信时延情况下混行车群采用MFRVI-CDM模型时跟随车辆与前车的状态差值变化曲线
Fig. 11 State error variation curves between following vehicles and preceding vehicles within the mixed vehicle groups using MFRVI-CDM model with communication delay
图 12 头车扰动情况下采用MFRVI-CDM模型时车辆的状态变化曲线
Fig. 12 State variation curves of vehicles using MFRVI-CDM model with disturbance of the leader vehicle
图 13 CAVs渗透率为0.2时采用MFRVI-CDM模型的车辆状态变化曲线
Fig. 13 State variation curves of vehicles using MFRVI-CDM model with a CAV penetration rate of 0.2
图 14 CAVs渗透率为0.5时采用MFRVI-CDM模型的车辆状态变化曲线
Fig. 14 State variation curves of vehicles using MFRVI-CDM model with a CAV penetration rate of 0.5
图 15 CAVs渗透率为0.8时采用MFRVI-CDM模型的车辆状态变化曲线
Fig. 15 State variation curves of vehicles using MFRVI-CDM model with a CAV penetration rate of 0.8
图 16 不同CAVs渗透率下采用三种模型的混行车群状态达到一致稳定所需时间
Fig. 16 Time required for mixed group state to reach consistent stability using three models under different CAV penetration rates
表 1 初始参数设置
Table 1 Initial parameter settings
参数 值 参数 值 $v_{\text{max}}$ $18\ \text{m/s}$ $v_{\text{des}}$ $12\ \text{m/s}$ $a_{\text{max}}$ $4\ \text{m/s}^2$ $d_{\text{des}}$ $26\ \text{m}$ $s_0$ $2\ \text{m}$ $\Delta t$ $0.01\ \text{s}$ $len$ $4\ \text{m}$ $TH$ $50\ \text{s}$ 
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表 2 异质车辆控制输入参数设置
Table 2 Control input parameter settings for heterogeneous vehicles
CAVs参数 值 HVs参数 值 $\alpha_c$ $0.49$ $\alpha_h$ $0.51$ $\beta_c$ $0.03$ $\beta_h$ $0.03$ $\gamma_c$ $2.36$ $\gamma_h$ $2.42$ $\mu_c$ $0.32$ $\mu_h$ $0.32$ $\rho_{c1}$ $0.97$ $\rho_{h1}$ $0.84$ $\rho_{c2}$ $0.03$ $\rho_{h2}$ $0.04$ $\zeta$ $0.03$ $\xi$ $0.32$
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