论文标题
最佳自动驾驶混合流量中最佳自动驾驶的能量和流动效应：车辆中的实验结果
Energy and Flow Effects of Optimal Automated Driving in Mixed Traffic: Vehicle-in-the-Loop Experimental Results
论文作者
论文摘要
本文在实验上证明了预期的辅助汽车算法在减少汽油发动机以及电动连接和自动化车辆（CAV）的能源使用方面的有效性，而无需牺牲安全性和交通流量。我们提出了一个循环（VIL）测试环境，其中实验骑士在轨道上驱动的实验骑兵实时与周围的虚拟流量相互作用。我们探讨了跟随城市和公路驱动周期以及微模拟产生的新兴高速公路交通时的节能。模型预测控制处理了高水平的速度计划，并通过对先前的CAV的通讯意图或先前的人类驱动车辆的估计可能运动进行了益处。经典反馈控制和数据驱动的非线性进料控制踏板的结合在低水平上实现了加速跟踪。控制器在ROS中实现，并通过校准的OBD-II读数来测量能量。与经过现实校准的人驾驶汽车的行动相比，我们报告的能源经济增长了多达30％，而无需牺牲进展。
This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), without sacrificing safety and traffic flow. We propose a Vehicle-in-the-Loop (VIL) testing environment in which experimental CAVs driven on a track interact with surrounding virtual traffic in real-time. We explore the energy savings when following city and highway drive cycles, as well as in emergent highway traffic created from microsimulations. Model predictive control handles high level velocity planning and benefits from communicated intentions of a preceding CAV or estimated probable motion of a preceding human driven vehicle. A combination of classical feedback control and data-driven nonlinear feedforward control of pedals achieve acceleration tracking at the low level. The controllers are implemented in ROS and energy is measured via calibrated OBD-II readings. We report up to 30% improved energy economy compared to realistically calibrated human driver car-following without sacrificing following headway.
