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

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

doi:10.1016/j.trc.2021.103168

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

Journal Article · Thu Jul 15 00:00:00 EDT 2021 · Transportation Research Part C: Emerging Technologies

DOI:https://doi.org/10.1016/j.trc.2021.103168· OSTI ID:1863155

^[1]; Guo, Longxiang ^[2]; ^[3]; ^[3]; Goulet, Nathan ^[2]; Jia, Yunyi ^[2]; ^[2]; ^[3]

Clemson Univ., SC (United States); Clemson University
Clemson Univ., Greenville, SC (United States)
Clemson Univ., SC (United States)

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. Here, we report up to 30% improved energy economy compared to realistically calibrated human driver car-following without sacrificing following headway.

View Accepted Manuscript (DOE)

Research Organization:: Clemson Univ., SC (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

Grant/Contract Number:: EE0008232

OSTI ID:: 1863155

Alternate ID(s):: OSTI ID: 1808232
OSTI ID: 1848816

Journal Information:: Transportation Research Part C: Emerging Technologies, Journal Name: Transportation Research Part C: Emerging Technologies Vol. 130; ISSN 0968-090X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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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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