Car-following model and optimization strategy for connected and automated vehicles under mixed traffic environment
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摘要: 为进一步提高混合交通环境下车辆的行车效率与交通流的稳定性，在考虑后视效应的基础上，融合多辆前车速度与加速度等状态信息，以指数平滑方式构建了网联自动驾驶车辆(CAV)跟驰模型；在此基础上，研究了前后方车辆数和状态信息完整度对模型稳定性的影响，结合Lyapunov第一方法和线性谐波微扰法进行了线性稳定性分析，并确定了模型最优参数；利用混合交通环境特性，在考虑通信信息丢失的情况下提出了CAV在不同位置和状态下的跟驰策略，并在该策略支撑下进行了不同CAV渗透率的车辆启动、车辆刹车停止、环形道路3个典型场景下的数值仿真。研究结果表明：在刹车停止场景中，全部车辆的停止波速最大提高了26.1%；在车辆启动场景中，启动波速最大提高了15.5%，车辆加速度和速度变化更为平缓；在环形道路场景中，当混合交通流中CAV渗透率由40%提高至100%时，在较大扰动条件下车辆的平均速度波动时间相较于低CAV渗透率场景下降了44.8%，波峰下降了5.7%，波谷上升了19.4%，而CAV渗透率较低时提出的优化策略对混合交通流的改善并不明显。由此可见，在当前构建实际混合交通环境与开展CAV实车试验比较困难的情况下，该跟驰模型和策略可用于车辆跟驰仿真与特定场景下的测试验证，能够有效保障混合交通环境中的交通流扰动吸收和车队稳定行驶。Abstract: To improve the driving efficiency and traffic flow stability of vehicles in the mixed traffic environment, the informations such as the velocities and accelerations of multiple vehicles in front were fused, and an exponential smoothing approach was adopted to build a car-following model of connected and automated vehicles (CAVs) based on the backward-looking effect. Then the effects of number of vehicles in front and behind and the completeness of state information on the model stability were studied. The linear stability analysis was carried out and the optimal parameters of the model were determined by combining the Lyapunov's first method and linear harmonic perturbation method. Combined with the characteristics of mixed traffic environments, the car-following strategies of CAVs in different positions and states were proposed in the condition of communication information loss. The numerical simulations were carried out in three typical scenarios with different CAV penetration rates, including vehicle starting, vehicle braking to stop, and circular road. Research results show that in the scenario of vehicle braking to stop, the maximum stopping wave speed of all vehicles increases by 26.1%. In the scenario of vehicle starting, the maximum starting wave speed increases by 15.5%, and the accelerations and speeds of vehicles change more smoothly. In the circular road scenario, when the CAV penetration rate in the mixed traffic flow increases from 40% to 100%, the fluctuation time of average speed of vehicles in the larger disturbance scenario decreases by 44.8%, the wave peak decreases by 5.7%, and the wave trough increases by 19.4%, compared to the low CAV penetration rate scenarios. However, the proposed optimization strategy does not significantly improve the mixed traffic flow at a lower CAV penetration rate. Thus, in the current situation where it is difficult to construct an actual mixed traffic environment and conduct CAV real vehicle tests, the model and strategies can be employed for the car-following simulation and test verification in specific scenarios to effectively guarantee the absorption of traffic flow disturbance and stable driving vehicles in the mixed traffic environment.
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图 4 不同CAV渗透率下的车辆减速度
Figure 4. Vehicle decelerations under different CAV penetration rates
图 5 刹车减速过程中车辆参数变化
Figure 5. Changes in vehicle parameters during braking and deceleration
图 6 不同CAV渗透率下的车辆加速度
Figure 6. Vehicle accelerations under different CAV penetration rates
图 10 轻微速度扰动下的混合车流速度
Figure 10. Speeds of mixed traffic flow under slight speed disturbance
图 11 较大速度干扰下的混合车流速度
Figure 11. Speeds of mixed traffic flow under large speed disturbance
表 1 模型稳定性参数对比
Table 1. Comparison of model stability parameters
模型 Vmax/ (m·s-1) Vmin/ (m·s-1) Vave/ (m·s-1) Rup/ % Rdown/ % BLOVD 0.851 5 0.678 1 0.799 7 6.50 15.18 OVCM 1.080 1 0.892 7 0.999 5 8.05 10.69 MHOVA 1.042 8 0.892 7 0.999 7 4.32 10.69 MVCM 1.036 3 0.895 7 0.999 7 3.67 10.39 MVISF 0.826 2 0.750 5 0.799 6 3.33 6.13 
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表 2 不同CAV渗透率下刹车停止场景的参数对比
Table 2. Parameter comparison of braking to stop scenario under different CAV penetration rates
CAV渗透率/% 平均加速度/(m·s-2) 波速/(m·s-1) 0 -0.504 10.32 50 -0.532 11.37 100 -0.548 13.01
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表 3 不同CAV渗透率下车辆启动场景的参数对比
Table 3. Comparison of parameters of vehicle starting scenario under different CAV penetration rates
CAV渗透率/% 平均加速度/(m·s-2) 波速/(m·s-1) 0 1.041 5.67 50 1.663 6.01 100 1.922 6.55
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