Jia, D, Chen, H orcid.org/0000-0003-0753-7735, Zheng, Z et al. (4 more authors) (2022) An Enhanced Predictive Cruise Control System Design with Data-Driven Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems, 23 (7). pp. 8170-8183. ISSN 1524-9050
Abstract
The predictive cruise control (PCC) is a promising method to optimize energy consumption of vehicles, especially the heavy-duty vehicles (HDV). Due to the limited sensing range and computational capabilities available on-board, the conventional PCC system can only obtain a sub-optimal speed trajectory based on a shorter prediction horizon. The recently emerging information and communication technologies such as vehicular communication, cloud computing, and Internet of Things provide huge potentials to improve the traditional PCC system. In this paper, we propose a general framework for the enhanced cloud-based PCC system which integrates a data-driven traffic predictive model and the instantaneous control algorithms. Specifically, we introduce a novel multi-view CNN deep learning algorithm to predict traffic situation based on the historical and real-time traffic data collected from fields, and the time-varying adaptive model predictive control (MPC) to calculate the instantaneous optimal speed profile with the aim of minimizing energy consumption. We verified our approach via simulations in which the impact of various traffic condition on the PCC-enabled HDV has been fully evaluated.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Predictive cruise control , cloud-based system , model predictive control , traffic prediction , deep learning algorithm |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number EU - European Union 713788 |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 May 2021 09:10 |
Last Modified: | 26 Jul 2022 13:45 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TITS.2021.3076494 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173584 |