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

Issam Bani; Belhassan Akrout; Walid Mahdi

doi:10.1007/978-3-030-11800-6_8

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

Issam Bani
, Belhassan Akrout
, Walid Mahdi

Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Advances in Predictive, Preventive and Personalised Medicine
Publisher	Springer Science and Business Media B.V.
Pages	69-77
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-030-11800-6_8
State	Published - 2019

Publication series

Name	Advances in Predictive, Preventive and Personalised Medicine
Volume	10
ISSN (Print)	2211-3495
ISSN (Electronic)	2211-3509

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Driver fatigue
Dynamic Bayesian network
Facial expressions
Hypovigilance

Access to Document

10.1007/978-3-030-11800-6_8

Cite this

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title = "Real-Time Driver Fatigue Monitoring with a Dynamic Bayesian Network Model",

abstract = "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.",

keywords = "Driver fatigue, Dynamic Bayesian network, Facial expressions, Hypovigilance",

author = "Issam Bani and Belhassan Akrout and Walid Mahdi",

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series = "Advances in Predictive, Preventive and Personalised Medicine",

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Real-Time Driver Fatigue Monitoring with a Dynamic Bayesian Network Model. / Bani, Issam; Akrout, Belhassan; Mahdi, Walid.
Advances in Predictive, Preventive and Personalised Medicine. Springer Science and Business Media B.V., 2019. p. 69-77 (Advances in Predictive, Preventive and Personalised Medicine; Vol. 10).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Real-Time Driver Fatigue Monitoring with a Dynamic Bayesian Network Model

AU - Bani, Issam

AU - Akrout, Belhassan

AU - Mahdi, Walid

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Driver fatigue

KW - Dynamic Bayesian network

KW - Facial expressions

KW - Hypovigilance

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U2 - 10.1007/978-3-030-11800-6_8

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BT - Advances in Predictive, Preventive and Personalised Medicine

PB - Springer Science and Business Media B.V.

ER -

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

Abstract

Publication series

UN SDGs

Keywords

Access to Document

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