智能网联环境下单交叉口车辆轨迹优化

Trajectory optimization of vehicles at isolated intersection in a connected and automated environment

为了提高信号灯前车辆的通行效率,改善交通流整体运行水平,本文从减少车辆延误和降低燃油消耗两个角度入手,在智能网联环境下,提出了一种车辆编组识别算法和针对编组头车的多目标线性轨迹优化模型(MOLP-pl)。首先对智能驾驶员跟驰模型(IDM)进行改进,调整车辆状态,减少车辆随机到达状态下车辆速度和车头时距分布的差异,同时为后续MOLP-pl轨迹优化模型的运行提供先决条件。在此基础上,以车辆编组为优化单元,通过车辆编组识别算法识别编组头车和跟随车辆,将编组头车的行驶轨迹作为优化对象并建立相应的数学模型。为了提高车辆轨迹优化模型的求解效率和精度,对其进行线性化重构,采用线性求解器计算编组头车加速度,构建编组头车最佳时空轨迹,然后,利用IDM跟驰模型计算跟随车辆的行驶速度,从而使编组车辆最大效率的通过交叉口。最后,利用SUMO构建的仿真实验表明:本研究提出的车辆轨迹优化算法可显著提高信号灯前车辆的通行效率,在三种不同的交通饱和度条件下,相对于无速度引导场景,车辆延误分别降低了8.56%、12.42%、64.79%,燃油消耗分别降低了17.21%、18.34%、12.64%;相对于逻辑控制场景,延误分别降低了-1.31%、2.63%、60.83%,燃油消耗分别降低了2.47%、7.91%、2.28%。

To enhance traffic efficiency at signal lights from the perspective of reducing vehicle delay and minimizing fuel consumption,this study proposes a vehicle platoon identification algorithm and a Multi-objective Linear Programming Trajectory Optimization Model for Platoon-leading Vehicles(MOLP-pl).First,the Intelligent Driver Model(IDM)is improved to adjust the vehicle state,reduce the differences in vehicle speed and headway distribution under random arrival conditions,and provide a prerequisite for the operation of the subsequent MOLP-pl trajectory optimization model.On this basis,the vehicle platoon identification algorithm is utilized to discern the leading and following vehicles,with the trajectory of the former serving as the optimization objective for establishing a corresponding mathematical model.Then,the vehicle trajectory optimization model is linearized and reconstructed to enhance efficiency and accuracy.Subsequently,a Liner Solver is employed to determine the acceleration of the leading vehicle,facilitating the construction of an optimal spatiotemporal trajectory.The IDM model is utilized to calculate the speed of the following vehicles.The simulation experiments conducted using SUMO demonstrate that:1)The vehicle trajectory optimization algorithm proposed in this study can significantly improve traffic efficiency at intersections.Under three different levels of traffic saturation,vehicle delay reduced by 8.56%,12.42%,and 64.79%,while fuel consumption decreased by 17.21%,18.34%,and12.64%,respectively,compared to the scenario without vehicle speed guidance;2)The vehicle delay reduced by-1.31%,2.63%,and 60.83%respectively,while the fuel consumption decreased by 2.47%,7.91%,and 2.28%in comparison to the logic-based control strategy.