Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection

  • Divya KanapramEmail author
  • Pablo Marin-Plaza
  • Lucio Marcenaro
  • David Martin
  • Arturo de la Escalera
  • Carlo Regazzoni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)


The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. Before the implementation of Machine Learning, the detection of abnormalities were manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. Nowadays, it is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving. In this paper, different scenarios have been set in order to train and test a switching DBN for Perimeter Monitoring Task using a semantic segmentation for the DBN model and Hellinger Distance metric for abnormality measurements.


Autonomous vehicles Intelligent Transportation System (ITS) Dynamic Bayesian Network (DBN) Hellinger distance Abnormality detection 



Supported by SEGVAUTO 4.0 P2018/EMT-4362) and CICYT projects (TRA2015-63708-R and TRA2016-78886-C3-1-R).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Divya Kanapram
    • 1
    • 3
    Email author
  • Pablo Marin-Plaza
    • 2
  • Lucio Marcenaro
    • 1
  • David Martin
    • 2
  • Arturo de la Escalera
    • 2
  • Carlo Regazzoni
    • 1
  1. 1.University of GenovaGenoaItaly
  2. 2.Universidad Carlos IIILeganesSpain
  3. 3.Queen Mary University of LondonLondonUK

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