智能网联环境下交叉口混行车队通行模型构建

Construction of Traffic Model for Mixed Platoon at Intersection under Intelligent Network Environment

为解决智能网联环境下交叉口自动驾驶车辆与网联车辆混行车队的通行问题,利用实时通信技术V2X(Vehicle to Everything)及混合整数规划、分支定界等方法,构建了智能网联环境下交叉口混行车队通行模型。同时,基于SUMO(Simulation of Urban Mobility)软件建立智能网联环境下交叉口混行车队通行仿真系统,并对所建模型进行了验证。最后,以十字交叉口为例,对比分析了同等环境下所建模型与自适应配时控制方案的平均延误、平均停车次数、平均能耗等参数差异。结果表明,在不同自动驾驶车辆渗透率下,所建模型能根据交通流状况对车队轨迹及信号配时进行优化,减少停车次数90%~94%、降低延误20%~30%、降低能耗10%~15%;随着自动驾驶车辆渗透率的增加,网联车辆的速度建议接受概率的影响在减弱,模型的优化效果也随之增加。

In order to solve the traffic problem of mixed platoon of connected and autonomous vehicles and connected vehicles at intersection under the intelligent network environment,a traffic model was built based on Vehicle to Everything(V2 X)real-time communication technology,mixed integer programming,branch and bound method.Meanwhile,a simulation system of mixed platoon at intersection under intelligent network environment was established using Simulation of Urban Mobility(SUMO)software for model verification.Taking one typical intersection as an example,the parameters such as average vehicle delay,average stop times and average energy consumption of the model and adaptive traffic signal control system were compared and analyzed.The results showed that under different penetration rates of connected and autonomous vehicles,the model could optimize the vehicle trajectory and signal timing according to the traffic flow conditions,and reduce the stop times by 90%~94%,vehicle delay by 20%~30%,and energy consumption by 10%~15%respectively;with the increasing of the penetration rate of autonomous vehicles,the influence of the acceptance probability of speed suggestions of networked vehicles was weakened,and the optimization effect of the model was increased.