基于排队长度估计的网联车辆节能车速规划方法

针对网联车辆在动态车流环境中多信号灯道路的车速规划问题，文中提出了一种基于路口排队长度实时估计的车辆节能车速规划方法. 首先，构建并训练用于路口排队长度估计的径向基神经网络；然后，在最优控制问题的框架下，实现车流排队和信号灯的联合建模，构建参考车速曲线优化问题；最后，利用提出的车速规划解耦变换求解方法，高效地获得参考车速曲线. 仿真结果表明，相比于传统的未考虑路口车流排队的节能车速规划方法，文中提出的方法可以产生更平滑的实际车速曲线，同时降低40%以上的能量消耗. 

An Eco-Driving Method with Queue Length Estimation for Connected Vehicles

Aiming at speed planning problems for connected vehicles traveling through multiple traffic signals under a dynamic traffic environment, an eco-driving method was proposed based on real-time queue length estimation. Firstly, a radial basis function neural network was constructed and trained to estimate queue length at intersection. Then, in the frame of optimal control, the traffic queuing was mathematically modeled together with traffic signals to formulate a speed profile optimization problem. Finally, the proposed decoupling transformation method was used to calculate a reference speed profile efficiently. Simulation results reveal that the proposed method can provide smoother actual speed profiles and save more than 40% energy compared with the traditional eco-driving method without considering the traffic queuing.