An Enhanced Predictive Cruise Control System Design With Data-Driven Traffic Prediction

Dongyao Jia*, Haibo Chen, Zuduo Zheng, David Watling, Richard Connors, Jianbing Gao, Ying Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)8170-8183
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number7
DOIs
Publication statusPublished - 1 Jul 2022
Externally publishedYes

Keywords

  • Predictive cruise control
  • cloud-based system
  • deep learning algorithm
  • model predictive control
  • traffic prediction

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