考虑时延速度差和限速信息的智能网联车跟驰模型

针对由于驾驶员对于道路限速和时延信息获取的不确定性而引起的跟驰行为受扰和交通流失稳等问题，提出了一种车联网（IoV）环境下考虑时延速度差和限速信息的跟驰模型TD-VDVL。首先，引入时延导致的速度变化量和道路限速信息对全速差（FVD）模型进行改进；然后，利用线性谱波微扰法推导出TD-VDVL模型的交通流稳定性判断依据，并分析模型中各参数对系统稳定性的影响；最后，利用Matlab进行数值仿真实验与对比分析。仿真实验中，分别选取在笔直道路和环形道路，给行驶过程中的车队施加轻微扰动。当条件一致时，TD-VDVL模型比优化速度（OV）、FVD模型中车队的速度波动率和车头间距起伏均小，尤其是当限速信息的敏感系数取0.3、时延速度差的敏感系数取0.3时，所提模型的车队速度平均波动率在时间500 s时可以达到2.35%，车头间距波峰波谷差仅为0.019 4 m。实验结果表明，TD-VDVL模型在引入时延速差和限速信息后，具备更优的稳定区域，能够明显增强跟驰车队吸收扰动的能力。

Car-following model of intelligent connected vehicles based on time-delayed velocity difference and velocity limit

Focusing on the problems of disturbed car-following behavior and instability of traffic flow caused by the uncertainty of the driver’s acquisition of road velocity limit and time delay information， a car-following model TD-VDVL （Time-Delayed Velocity Difference and Velocity limit） was proposed with the consideration of the time-delayed velocity difference and the velocity limit information in the Internet of Vehicles （IoV） environment. Firstly， the speed change caused by time delay and road velocity limit information were introduced to improve the Full Velocity Difference （FVD） model. Then， the linear spectrum wave perturbation method was used to derive the traffic flow stability judgment basis of TD-VDVL model， and the influence of each parameter in the model on the stability of the system was analyzed separately. Finally， the numerical simulation experiments and comparative analysis were carried out using Matlab. In the simulation experiments， straight roads and circular roads were selected， and slight disturbance was imposed on the fleet during driving. When conditions were the same， TD-VDVL model had the smallest velocity fluctuation rate and the fluctuation of fleet headway compared to the Optimal Velocity （OV） and FVD models. Especially when the sensitivity coefficient of the velocity limit information was 0.3， and the sensitivity coefficient of the time-delayed speed difference was 0.3， the proposed model had the average fluctuation rate of the fleet velocity reached 2.35% at time of 500 s， and the peak and valley difference of fleet headway of only 0.019 4 m. Experimental results show that TD-VDVL model has a better stable area after introducing time-delayed velocity difference and velocity limit information， and can significantly enhance the ability of car-following fleet to absorb disturbance.