Modeling AVs & RVs’ car-following behavior by considering impacts of multiple surrounding vehicles and driving characteristics
Modeling AVs & RVs’ car-following behavior by considering impacts of multiple surrounding vehicles and driving characteristics
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AV 建模
DOI：
10.1016/j.physa.2021.126625
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发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Meng Zeng
中科院分区：
文献类型：
--
作者：
Fang Zong;Meng Wang;Jinjun Tang;Meng Zeng
提出了一种基于全速度加速度差模型（FVAD）的混合车辆跟驰模型，用于描述常规车辆（RV）和自动驾驶车辆（AV）的微观跟驰行为。该模型考虑了多辆前车和一辆后车的速度，以及前车与宿主车之间的速度差、加速度差和车头时距。对于自动驾驶汽车的跟驰模型，引入分子动力学理论，定量地表达了多辆前车对宿主车的影响。用多辆前车的速度和每辆前车与宿主车之间的车头时距来表示这种影响。此外，我们在建立RV模型时考虑了驾驶员的跟车风格。稳定性分析结果表明，该模型下交通流的稳定性不易受速度变化的影响，优于FVAD模型。根据多车混合跟驰的现场试验数据，得到了模型中参数的最优值，并通过数值仿真验证了模型的拟合精度。结果表明，与FVAD模型相比，RV模型的平均最大误差（MME）和平均误差（ME）分别降低了39.50%和13.12%，精度提高了14.48%。与ACC（Adaptive Cruise Control）模型相比，AV模型控制的加速策略更加平滑。有效的跟驰行为控制有助于提高无人驾驶汽车的运行效率，增强交通流的稳定性。此外，该模型还可用于在房车和自动驾驶汽车流量不均匀的情况下进行车队控制。该模型还可以作为一种工具来模拟车辆跟驰行为，这是有益的道路交通管理和基础设施布局的AV和RV混合交通环境。
This paper proposes a mixed-vehicles car-following model based on FVAD (Full Velocity and Acceleration Difference) model to describe the microscopic car-following behavior of RVs (Regular Vehicles) and AVs (Autonomous Vehicles). The model involves the velocity of multiple front vehicles and a rear vehicle as well as the velocity difference, acceleration difference and headway between each front vehicle and the host vehicle. As for AV’s car-following model we introduced the molecular dynamic theory to quantitatively express the influence of multiple front vehicles on the host vehicle. The velocity of multiple front vehicles and headway between each of them and the host vehicle are used to express the influence. Besides, we consider drivers’ car-following styles in constructing the RV model. The stability analysis results indicate that the stability of traffic flow under the proposed model is not easily affected by the change in velocity, and is better than FVAD model. According to the data collected from the car-following field test mixed with AVs and RVs, we obtain the optimal value of the parameters in the model, and examine the fitting accuracy with a numerical simulation. The results indicate that compared with FVAD model, the MME (Mean Maximum Error) and ME (Mean Error) of RV model is reduced by 39.50% and 13.12%, respectively, and the accuracy is improved by 14.48%. The acceleration strategy controlled by the AV model is smoother than by the ACC (Adaptive Cruise Control) model. With effective car-following behavior control, it is helpful to improve the operation efficiency of AVs and enhance the stability of traffic flow. Additionally, the model can be utilized for platoon control in the case of RVs’ and AVs’ heterogeneous flow. This model can also serve as a tool to simulate car-following behavior, which is beneficial for road traffic management and infrastructure layout in AV and RV mixed traffic environment.
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