AutoDetect: A Novel Real-Time Intelligent Sensor Failure Detection for Connected Vehicles

Matthew Robinson, Pedram Asef, Mohammad Shojafar, Zahra Pooranian, Harry Lees, Mark Longden

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

Abstract

This study addresses the need for developing new frameworks to monitor and detect sensor failures in connected commercial vehicles (CCV)s. The CCV’s sensor health is more important when performance predictions and other communication-related errors (e.g. cyber-physical attacks) can manipulate the sensory network’s resiliency. We developed a novel machine learning (ML)-based framework, AutoDetect, to equip the cloud-tied operators with tools for understanding the abnormal sensor data streaming from the vehicle on the cloud level which explains the sensor data errors due to sensor failures only. We developed an innovative autoencoder (AE) neural network algorithm coupled with K-means clustering to create patterns. To learn the relationship between operating samples and features, when streaming sensor data over high-dimensional datasets is collected in the United Kingdom (UK). Different profiles of sensor data are collected under various driving conditions to monitor the ground truth of the sensor’s confidence levels in CCVs. The new AutoDetect tracked real-time sensor failures with a minimum accuracy of 90%.
Original languageEnglish
Title of host publication2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)
Place of PublicationMA, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)979-8-3503-3179-0
ISBN (Print)979-8-3503-3180-6
DOIs
Publication statusPublished - 21 May 2023
Event2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET) - London, United Kingdom
Duration: 19 May 202321 May 2023

Conference

Conference2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)
Abbreviated titleGlobConET 2023
Country/TerritoryUnited Kingdom
CityLondon
Period19/05/2321/05/23

Keywords

  • Cloud computing
  • Software algorithms
  • Neural networks
  • Decision making
  • Clustering algorithms
  • Prediction algorithms
  • Real-time systems

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