Real-Time Driver Fatigue Monitoring with a Dynamic Bayesian Network Model

  • Issam Bani
  • Belhassan Akrout
  • Walid Mahdi
Conference paper
Part of the Advances in Predictive, Preventive and Personalised Medicine book series (APPPM, volume 10)


In this chapter, our objective is to detect the driver fatigue state. To this end, we have integrated the most relevant causes and effects of fatigue in a dynamic Bayesian network. We used the following as the main causes of drowsiness: sleep quality, road environment, and driving duration. On the other hand, we added as consequences real-time facial expressions, such as blinking, yawning, gaze, and head position. The result obtained changes over time and it is repeatedly included in the model to calculate fatigue level. In comparison with a realistic simulation, this model is very effective at detecting driver fatigue.


Driver fatigue Hypovigilance Facial expressions Dynamic Bayesian network 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Issam Bani
    • 1
  • Belhassan Akrout
    • 1
    • 2
  • Walid Mahdi
    • 1
    • 3
  1. 1.Laboratory MIRACL, Institute of Computer Science and Multimedia of SfaxSfax UniversitySfaxTunisia
  2. 2.College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia
  3. 3.Department of Computer ScienceTaif UniversityTaifSaudi Arabia

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