@inproceedings{31b14a1a35c34863b5b62a4eadf5d0d4,
title = "Decentralized Model Predictive Control for Automated and Connected Electric Vehicles at Signal-free Intersections",
abstract = "The development of connected and automated vehicles (CAVs) enables improvements in the safety, smoothness, and energy efficiency of the road transportation systems. This paper addresses the problem of optimally controlling battery-electric CAVs crossing an unsignalized intersection subject to a first-in-first-out crossing policy. The optimal velocity trajectory of each vehicle that minimizes the average energy consumption and travel time, is found by a decentralized model predictive control (DMPC) method via a convex modeling framework so as to ensure computational efficiency and the optimality of the solution. Numerical examples and comparisons with a centralized control counterpart demonstrate the effectiveness of the proposed decentralized coordination scheme and the trade-off between energy consumption and travel time. Further investigation into the size of the sampling interval is also provided in order to show the validity of the method in practice.",
author = "Xiao Pan and Boli Chen and Li Dai and Stelios Timotheou and Evangelou, \{Simos A.\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 60th IEEE Conference on Decision and Control, CDC 2021 ; Conference date: 13-12-2021 Through 17-12-2021",
year = "2021",
doi = "10.1109/CDC45484.2021.9683495",
language = "English",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2659--2664",
booktitle = "60th IEEE Conference on Decision and Control, CDC 2021",
address = "United States",
}
