TY - JOUR
T1 - An Enhanced Predictive Cruise Control System Design With Data-Driven Traffic Prediction
AU - Jia, Dongyao
AU - Chen, Haibo
AU - Zheng, Zuduo
AU - Watling, David
AU - Connors, Richard
AU - Gao, Jianbing
AU - Li, Ying
N1 - Funding Information:
This work was supported in part by the EU project optiTruck under Grant H2020/713788, in part by the EU project MODALES under Grant H2020/815189, in part by the Australian Research Council under Grant DP210102970, and in part by Dynnoteq Limited
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - Predictive cruise control
KW - cloud-based system
KW - deep learning algorithm
KW - model predictive control
KW - traffic prediction
UR - http://www.scopus.com/inward/record.url?scp=85105852602&partnerID=8YFLogxK
U2 - 10.1109/TITS.2021.3076494
DO - 10.1109/TITS.2021.3076494
M3 - Article
AN - SCOPUS:85105852602
SN - 1524-9050
VL - 23
SP - 8170
EP - 8183
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 7
ER -