Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results. (September 2021)

• Record Type: Journal Article
• Authors: Ard, Tyler; Guo, Longxiang; Dollar, Robert Austin; Fayazi, Alireza; Goulet, Nathan; Jia, Yunyi; Ayalew, Beshah; Vahidi, Ardalan
• Is Part Of: Transportation research. Volume 130(2021)
• Publication Date: 2021-09
• Subjects: Vehicle-in-the-loop -- Virtual traffic microsimulation -- Probabilistic constraints -- Energy efficiency -- Model predictive control -- Data-driven control; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103168
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Embeds physical vehicles into a virtual traffic scene for energy and flow evaluation. Measures conventional and electric powertrain types for energy effects. Introduces probabilistic constraints to balance safety and traffic flow considerations. Fuses data-driven techniques and classical techniques for automated vehicle control. Abstract: 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 implement 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.