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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ji Q, Lan P, Looney C (2006) A probabilistic framework for modeling and real-time monitoring human fatigue. IEEE Trans Syst Man Cybern-Part A Syst Hum 36(5):862–875
Yang G, Lin Y, Bhattacharya P (2010) A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Inf Sci 180(10):1942–1954
Gang L, Song X, Zhang M, Yao B, Zhou L (2017) Analysis of driver fatigue causations based on the Bayesian network model. Simulation 93(7):553–565
He Q, Li W, Fan X, Fei Z (2014) Driver fatigue evaluation model with integration of multi-indicators based on dynamic Bayesian network. IET Intell Transp Syst 9(5):547–554
Li X, Ji Q (2005) Active affective state detection and user assistance with dynamic Bayesian networks. IEEE Trans Syst Man Cybern-Part A Syst Hum 35(1):93–105
Guo W, Zhang B, Xia L, Shi S, Zhang X, She J (2016) Driver drowsiness detection model identification with Bayesian network structure learning method. In: Control and Decision Conference (CCDC), 2016 Chinese. IEEE, pp 131–136
Safety Board (1999) Evaluation of US department of transportation efforts in the 1990s to address operator fatigue. Safety Report NTSB/SR-99/01, Washington, DC
Charlton SG, Ashton ME (1998) Review of fatigue management strategies in the transport industry. Land transport safety authority, Wellington, New Zealand, Technical Report, pp 97–91
Lan P, Ji Q, Looney CG (2002) Information fusion with Bayesian networks for monitoring human fatigue. In: Proceedings of the Fifth International Conference on Information Fusion, 2002, vol 1. IEEE, pp 535–542
Philip P, Sagaspe P, Moore N, Taillard J, Charles A, Guilleminault C, Bioulac B (2005) Fatigue, sleep restriction and driving performance. Accid Anal Prev 37(3):473–478
Cabon P (1992) Maintien de la vigilance et gestion du sommeil dans les systemes automatises: recherche de laboratoire applications aux transports ferroviaires et aeriens. PhD thesis, Paris 5
Akerstedt T, Czeisler CA, Dinges DF, Horne JA (1994) Accidents and sleepiness: a consensus statement from the International Conference on Work Hours, Sleepiness and Accidents, Stockholm. J Sleep Res 3(4):195
Trutschel U, Sirois B, Sommer D, Golz M, Edwards D (2011) PERCLOS: an alertness measure of the past. In: Proceedings of the sixth international driving symposium on human factors in driver assessment, training and vehicle design. University of Iowa, pp 172–79
Akrout B, Mahdi W (2013) A blinking measurement method for driver drowsiness detection. In: Proceedings of the 8th International Conference on Computer Recognition Systems CORES. Springer, pp 651–660
Momin BF, Abhyankar PP (2012) Current status and future research directions in monitoring vigilance of individual or mass audience in monotonous working environment. Int J Soft Comput 3(2):45
Akrout B, Mahdi W (2015) Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration. Mach Vis Appl 26(1):1–13
Dean T, Kanazawa K (1989) A model for reasoning about persistence and causation. Comput Intell 5(2):142–150
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bani, I., Akrout, B., Mahdi, W. (2019). Real-Time Driver Fatigue Monitoring with a Dynamic Bayesian Network Model. In: Chaari, L. (eds) Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Advances in Predictive, Preventive and Personalised Medicine, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-11800-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-11800-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11799-3
Online ISBN: 978-3-030-11800-6
eBook Packages: MedicineMedicine (R0)