考虑时延和多前车反馈的智能网联汽车跟驰模型

Car-following model of intelligent connected vehicles considering time delay and multiple front vehicle feedbacks

针对智能网联环境下传感器感知和车车通信(vehicle to vehicle,V2V)都存在时延的问题,提出一种考虑双时延和多前车反馈(dual delay multiple look-ahead full velocity difference,DD-MLFVD)的智能网联汽车跟驰模型.根据智能网联汽车感知特性引入双时延信息,结合多前车速度差和期望速度信息提出DD-MLFVD模型.通过微小扰动法求解DD-MLFVD模型的临界稳定性条件,同时结合模型参数研究前车数量和时延大小对模型稳定域的影响.利用直道场景对模型进行仿真分析,着重研究变扰动和变时延场景下DD-MLFVD对交通流的稳定效果.结果表明:面对复杂扰动影响,DD-MLFVD模型能够较好吸收扰动,可提升交通流的稳定性.

To solve the delay problem between sensor perception and V2V communication in the intelligent networked environment,the dual delay multiple look-ahead full velocity difference(DD-MLFVD) model was proposed with considering dual delay and multiple front vehicle feedbacks.The dual delay information was introduced according to the sensing characteristics of intelligent connected vehicles,and the DD-MLFVD model was proposed by combining the multi-vehicle speed differences and the desired speeds.The tiny perturbation method was utilized to solve the critical stability conditions of the DD-MLFVD model,and the effects of the vehicle number in front of ego vehicle and the delay value on the stability domain of the model were investigated.The model was simulated and analyzed by the straight road scenario,and the stability effect of DD-MLFVD on traffic flow under variable disturbance and variable delay scenarios was emphatically investigated.The results show that by the proposed DD-MLFVD model,the disturbances can be well absorbed in the face of complex disturbances,and the stability of traffic flow can be improved.