智能网联车环境下交叉口车流轨迹优化模型

Vehicle Trajectory Optimization Model for Intersection under the Connected and Automated Vehicles Environment

在智能网联环境下,车辆可通过相互穿插和协作通过交叉口,无需信号灯控制。为保证车辆安全高效运行,建立车辆到达时序和速度协同优化的交叉口车流轨迹优化模型。提出车辆到达时序优化模型和车辆速度优化模型,建立车辆到达时刻与速度的函数关系;在此基础上,模型以所有车辆在控制区域的行程时间与油耗加权最小为目标,车辆路径、到达时刻和速度等关键参数为决策变量,设计迭代式算法求解,实现同时优化车辆到达时刻和速度且交叉口运行效益最大的目的。实验结果表明,与车辆时序和轨迹分别优化的两阶段模型相比,本文模型降低车均延误32.1%,减少车均油耗9.9%,说明该模型具有良好的主动性和适应性,在降低车辆延误的同时也节省了油耗。

Under the connected and automated driving environment,vehicles can cross the intersection with good coordination and minimal controls from traditional traffic signals.To ensure the safe and efficient vehicle operations at intersections,this study proposes a trajectory optimization model to optimize vehicle arrival time and speed.The vehicle arrival time sequence optimization model and the vehicle speed optimization model are developed to establish the functional relation between vehicle arrival time and speed.Then,the weighted sum of all vehicle travel time and fuel consumptions are set as the objective of the proposed model.The decision variables include vehicle route,arrival time,and speed.An iterative algorithm is designed to optimize both the vehicle arrival time and speed,and maximize the operation benefit at the intersection.Compared with the results from the two-level trajectory optimization model,the proposed model reduced the average delay by 32.0%and reduced the fuel consumption by 9.9%.The proposed model has good flexibility and mobility,which can reduce both vehicle delays and fuel consumptions.