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Sensor Fault Diagnosis of Connected Vehicles Under Imperfect Communication Network

[+] Author Affiliations
Zoleikha Abdollahi Biron, Pierluigi Pisu

Clemson University, Greenville, SC

Satadru Dey

University of California, Berkeley, Berkeley, CA

Paper No. DSCC2016-9822, pp. V001T16A003; 8 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME


Connected vehicles are one of the promising technologies for future Intelligent Transportation Systems (ITS). Despite being the potentially beneficial in creating an efficient, sustainable and green transportation system, connected vehicles presents a set of specific challenges from safety and reliability standpoint. The first challenge arises from the information lost due to unreliable communication network which affects the control/management system of the individual vehicles and the overall system. Secondly, faulty sensors can affect the individual vehicle’s safe operation and in turn will create a potentially unsafe node in the vehicular network. Therefore, it is of utmost importance to take these issues into consideration while designing the control/management algorithms of the individual vehicles as a part of connected vehicle system. In this paper, we consider a connected vehicle system under Co-operative Adaptive Cruise Control (CACC) and propose a diagnostic scheme that deals with these aforementioned challenges. The effectiveness of the overall diagnostic scheme is verified via simulation studies.

Copyright © 2016 by ASME



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