Literature Database Entry

kennedy2026decentralized

Justin M. Kennedy, Lixin Yang, Daniel E. Quevedo and Falko Dressler, "Decentralized Model Predictive Control for Platooning: Enhancing Human-Driver Collaboration," IEEE Transactions on Control Systems Technology, 2026. (to appear)

Abstract

Recent advances in cooperative adaptive cruise control have demonstrated the potential for vehicle platooning to revolutionize road transportation through enhanced safety, reduced congestion, and improved energy efficiency. While autonomous vehicle technology continues to evolve rapidly, current regulatory frameworks and safety considerations necessitate the human driver supervision. This creates a unique challenge in developing control systems that can effectively balance autonomous operation with human intervention. To enhance the human-driver collaboration with autonomous vehicle platooning, in this paper, we present a novel decentralized model predictive control framework that explicitly incorporates human-driver interaction while maintaining desired inter-vehicle distances and velocities in platoon formations. This framework employs a distributed architecture where each vehicle operates independently and exchanges local measurements through vehicle-to-vehicle communication. To overcome the inherent unreliability of wireless communications in real-world scenarios, we develop a robust distributed state estimation strategy. This approach enables each vehicle to combine local sensor measurements with received data to construct accurate estimates of the full platoon state. Based on these estimates, vehicles compute optimal control actions locally while achieving performance comparable to an ideal centralized controller with perfect communication. Through extensive Plexe simulations, we demonstrate that the proposed decentralized model predictive controller achieves comparable performance to the ideal centralized case, even under partial state information and communication constraints.

Quick access

BibTeX BibTeX

Contact

Justin M. Kennedy
Lixin Yang
Daniel E. Quevedo
Falko Dressler

BibTeX reference

@article{kennedy2026decentralized,
    author = {Kennedy, Justin M. and Yang, Lixin and Quevedo, Daniel E. and Dressler, Falko},
    note = {to appear},
    title = {{Decentralized Model Predictive Control for Platooning: Enhancing Human-Driver Collaboration}},
    journal = {IEEE Transactions on Control Systems Technology},
    issn = {1063-6536},
    publisher = {IEEE},
    year = {2026},
   }
   
   

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.

Last modified: 2026-03-05