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.

中文翻译：

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混合交通中最佳自动驾驶的能量和流量效应：车辆在环实验结果

本文通过实验证明了预期汽车跟随算法在减少汽油发动机和电动互联和自动车辆 (CAV) 的能源使用方面的有效性，而不会牺牲安全性和交通流量。我们提出了一种车辆在环 (VIL) 测试环境，在该环境中，在轨道上行驶的实验性 CAV 与周围的虚拟交通实时交互。我们探索了在遵循城市和高速公路驾驶循环以及通过微观模拟创建的紧急高速公路交通时的节能效果。模型预测控制处理高水平的速度规划，并从前面 CAV 的传达意图或前面的人类驾驶车辆的估计可能运动中受益。踏板的经典反馈控制和数据驱动的非线性前馈控制相结合，实现了低水平的加速度跟踪。控制器在 ROS 中实现，能量通过校准的 OBD-II 读数测量。我们报告说，与实际校准的人类驾驶员跟车相比，能源经济性提高了 30%，而不会牺牲跟车速度。