2020/09/16 by Tyler Ard, Longxiang Guo, Ard, Tyler +13
Engineering · #Autonomous Vehicle Technology and Safety #FOS: Electrical engineering #Real-time simulation and control systems #Systems and Control (eess.SY) #Traffic control and management #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.07872
openalex publication_date 2020/09/16 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
This paper experimentally demonstrates the effectiveness of an anticipative\ncar-following algorithm in reducing energy use of gasoline engine and electric\nConnected and Automated Vehicles (CAV), without sacrificing safety and traffic\nflow. We propose a Vehicle-in-the-Loop (VIL) testing environment in which\nexperimental CAVs driven on a track interact with surrounding virtual traffic\nin real-time. We explore the energy savings when following city and highway\ndrive cycles, as well as in emergent highway traffic created from\nmicrosimulations. Model predictive control handles high level velocity planning\nand benefits from communicated intentions of a preceding CAV or estimated\nprobable motion of a preceding human driven vehicle. A combination of classical\nfeedback control and data-driven nonlinear feedforward control of pedals\nachieve acceleration tracking at the low level. The controllers are implemented\nin ROS and energy is measured via calibrated OBD-II readings. We report up to\n30% improved energy economy compared to realistically calibrated human driver\ncar-following without sacrificing following headway.\n