混合交通流环境下单交叉口自动驾驶车辆轨迹优化

信号交叉口对城市道路的通行能力以及车辆的燃油消耗具有重要影响。本文提出一种在自动驾驶车辆和人工驾驶车辆混合交通流环境下的自动驾驶车辆的轨迹优化方法。基于交叉口信号灯的配时方案，构建车辆旅行时间估计模型，并以自动驾驶车辆燃油消耗最小以及通行效率最大为目标，构建自动驾驶车辆轨迹优化模型，对车辆进行动态轨迹规划和控制。车辆轨迹滚动优化模型采用高斯伪谱法进行离散化求解，并基于SUMO仿真平台对模型结果进行验证。仿真结果表明，自动驾驶车辆可以通过优化自身控制变量影响人工驾驶车辆的运行状态，减少交通流的排队以及时走时停现象。本文提出的车辆轨迹优化方法对于降低车队整体燃油消耗、提升车队平均速度、缩短平均行程时间具有重要作用。

Trajectory Optimization of Connected Vehicles at Isolated Intersection in Mixed Traffic Environment

Signalized intersection has an important impact on urban road capacity and vehicle fuel consumption. This paper presents a trajectory optimization method for connected vehicles in mixed traffic flow. , The vehicle travel time estimation model was developed considering the traffic signal timing plan. The trajectory planning and connected vehicle control were carried out with the goal of minimizing fuel consumption and maximizing traffic operation efficiency. The Gaussian Pseudo-Spectral method was used to discretize the vehicle trajectory rolling optimization model. The Simulation of Urban Mobility (SUMO) simulation platform was used to verify the model results. The simulation results show that the connected vehicle can optimize control variables to affect the state of the human-driven vehicle, and reduce the queuing of traffic flow and the stop-and-go phenomenon. The proposed vehicle trajectory optimization method is important to reduce the overall fuel consumption of the fleet, improve the average speed of the fleet and shorten the average travel time.