A distributed local Kalman consensus filter for traffic estimation

Ye Sun, Daniel B. Work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) is used to describe the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, we prove that the estimates given by the DLKCF are ultimately bounded. Numerical experiments are provided to show the asymptotic stability of the DLKCF for observable modes, and illustrate the effect of the DLKCF on promoting consensus among various local agents. Supplementary source code is available at https://github.com/yesun/DLKCFcdc2014.

Original languageEnglish (US)
Title of host publication53rd IEEE Conference on Decision and Control,CDC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6484-6491
Number of pages8
EditionFebruary
ISBN (Electronic)9781479977468
DOIs
StatePublished - 2014
Externally publishedYes
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

Publication series

NameProceedings of the IEEE Conference on Decision and Control
NumberFebruary
Volume2015-February
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Other

Other2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014
Country/TerritoryUnited States
CityLos Angeles
Period12/15/1412/17/14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint

Dive into the research topics of 'A distributed local Kalman consensus filter for traffic estimation'. Together they form a unique fingerprint.

Cite this