Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results

Tyler Ard; Longxiang Guo; Robert Austin Dollar; Alireza Fayazi; Nathan Goulet; Yunyi Jia; Beshah Ayalew; Ardalan Vahidi

doi:10.1016/j.trc.2021.103168

Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results

Tyler Ard, Longxiang Guo, Robert Austin Dollar, Alireza Fayazi, Nathan Goulet, Yunyi Jia, Beshah Ayalew, Ardalan Vahidi

Applied Research for Mobility Systems

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

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.

Original language	English
Article number	103168
Journal	Transportation Research Part C: Emerging Technologies
Volume	130
DOIs	https://doi.org/10.1016/j.trc.2021.103168
State	Published - Sep 2021

Funding

This work was supported by an award from the U.S. Department of Energy Vehicle Technologies Office (Project DE-EE0008469). The authors would like to thank Dr. Joachim Taiber and the International Transportation Innovation Center (ITIC) for reduced-fee access to the testing grounds throughout the project. They also thank Mr. Dominik Karbowski for contributing to discussion, thank Mr. David Mann for his technical support in dynamometer testing, and thank Professor Zoran Filipi for access to the International Center for Automotive Research (ICAR) facilities.

Keywords

Data-driven control
Energy efficiency
Model predictive control
Probabilistic constraints
Vehicle-in-the-loop
Virtual traffic microsimulation

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10.1016/j.trc.2021.103168

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title = "Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results",

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.",

keywords = "Data-driven control, Energy efficiency, Model predictive control, Probabilistic constraints, Vehicle-in-the-loop, Virtual traffic microsimulation",

author = "Tyler Ard and Longxiang Guo and Dollar, {Robert Austin} and Alireza Fayazi and Nathan Goulet and Yunyi Jia and Beshah Ayalew and Ardalan Vahidi",

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year = "2021",

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AU - Fayazi, Alireza

AU - Goulet, Nathan

AU - Jia, Yunyi

AU - Ayalew, Beshah

AU - Vahidi, Ardalan

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N2 - 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.

AB - 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.

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KW - Energy efficiency

KW - Model predictive control

KW - Probabilistic constraints

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Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results

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