Energy-efficient reactive and predictive connected cruise control
Shen, Minghao Dollar, Robert Austin Molnar, Tamas G. He, Chaozhe R. Vahidi, Ardalan Orosz, Gábor
Shen, Minghao
Dollar, Robert Austin
Molnar, Tamas G.
He, Chaozhe R.
Vahidi, Ardalan
Orosz, Gábor
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2024-01
Type
Article
Genre
Keywords
Connected automated vehicles,Vehicle-to-vehicle connectivity,Traffic flow models,Intelligent vehicles,Automation
Subjects (LCSH)
Citation
M. Shen, R. A. Dollar, T. G. Molnar, C. R. He, A. Vahidi and G. Orosz, "Energy-Efficient Reactive and Predictive Connected Cruise Control," in IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 944-957, Jan. 2024, doi: 10.1109/TIV.2023.3281763
Abstract
Connected and automated vehicles (CAVs) have shown great potential in improving the energy efficiency of road transportation. Energy savings, however, greatly depends on driving behavior. Therefore, the controllers of CAVs must be carefully designed to fully leverage the benefits of connectivity and automation, especially if CAVs travel amongst other non-connected and human-driven vehicles. With this as motivation, we introduce a framework for the longitudinal control of CAVs traveling in mixed traffic including connected and non-connected human-driven vehicles. Reactive and predictive connected cruise control strategies are proposed. Reactive controllers are given by explicit feedback control laws. Predictive controllers, on the other hand, optimize the control input in a receding-horizon fashion, by predicting the motions of preceding vehicles. Beyond-line-of-sight information obtained via vehicle-to-vehicle (V2V) communication is leveraged by the proposed reactive and predictive controllers. Simulations utilizing real traffic data show that connectivity can bring up to 30% energy savings in certain scenarios.
Table of Contents
Description
Published online on August 15, 2023. Published in the journal in January 2024.
Publisher
IEEE
Journal
IEEE Transactions on Intelligent Vehicles
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
ISSN
2379-8904
2379-8858
2379-8858