Integrating a HMM-Based Driver Fatigue Recognition Model into Smart Vehicle Space for Ubiquitous Computing

Abstract:

Smart Vehicle Space (SVS) is a new smart space adopting vehicles as environment carrier, with embedded abilities of computing, communication, and perception. This paper proposes a model of driver fatigue recognition based on Hidden Markov Model (HMM) for SVS. We selected the PERCLOS feature variables as low-level contexts for driver fatigue evaluation, and established the HMM through a large number of training sample data. Then we identified the most likely driver's hidden states (high-level contexts) from the observation sequence using the Viterbi algorithm, to remind drivers to ensure the safe driving behavior. Finally, a case study in the simulation environment is given, which confirmed that the application can identify the driver's body states with a high probability, as well as maintain the good recognition effect in case of several invalid variables in observation sequence.

• Periodical: Key Engineering Materials (Volumes 467-469)
• Pages: 23-30
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.467-469.23
• Online since: February 2011
• Authors: Qing Wu, Wei Wei Yu
• Keywords: Driver Fatigue, HMM, Smart Vehicle Space, Ubiquitous Computing

References

[1] Dj.M. Maric, P.F. Meier and S.K. Estreicher: Mater. Sci. Forum Vol. 83-87 (1992), p.119.

[1] NIST. Smart space project [EB/OL]. http: /www. nist. gov/smart space, June 25, (2010).

[2] MIT. Project oxygen [EB/OL]. http: /oxygen. lcs. mit. edu, June 25, (2010).

[3] Microsoft Research. Easy living project[EB/OL]. http: /research. microsoft. com/easyliving/, June 25, (2010).

[4] Tsinghua University, Smart classroom project[EB/OL]. http: /media. cs. tsinghua. edu. cn /pervasive/ projects/classroom/ index. html, April 15, (2010).

[5] WuQing, Research on Model and Methodology of Adaptive Middleware for Ubiquitous Computing, Zhejiang University, (2006).

[6] L R Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition[C]. Proceedings of the IEEE, 77(2) : 257, (1989).

DOI: 10.1109/5.18626

[7] Xiaodong He, Mei Yang, Jianfeng Gao, Patrick Nguyen, Robert Moore. Indirect-HMM-based hypothesis alignment for combining outputs from machine translation systems[C]. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Pages: 949-957, (2008).

DOI: 10.3115/1613715.1613730

[8] Yuexian ZOU, Guangyi SHI, Hang SHI, and Yiyan WANG. Image Sequences Based Traffic Incident Detection for Signaled Intersections Using HMM[C], Hybrid Intelligent Systems, Vol. 1, pp.257-261, (2009).

DOI: 10.1109/his.2009.58

[9] Muhonen, K., Dunnihoo, J., Grund, E., Peachey, N., Brankov, A. Failure detection with HMM waveforms[C], EOS/ESD Symposium, 2009 31st: 1-9.

[10] FORNEY G David. The viterbi algorithm [J]. Proc. IEEE, 1973, 61(3)：268-278.

[11] D. Hernando, V. Crespi, and G. Cybenko, Efficient Computation of the Hidden Markov Model Entropy for a Given ObservationSequence, IEEE Trans. Information Theory, vol. 51, no. 7, pp.2681-2685, July (2005).

DOI: 10.1109/tit.2005.850223

[12] L.M. Bergasa, J. Nuevo, M.A. Sotalo, and M. Vazquez, Real-time system for monitoring driver vigilance, in Proc. Intelligent Vehicle Symp., Parma, Italy, pp.78-83, (2004).

DOI: 10.1109/ivs.2004.1336359

[13] DINGES D F, GRACE R. PERCLOS: a valid psychophysiological measure of alertness as assessed by psychomotor vigilance[R]. National Highway Traffic Safety Administration Final Report 1998, Report No. FHWA-MCRT-98-006.

DOI: 10.1037/e449092008-001

[14] Tapas Kanungo. UMDHMM [EB/OL], http: /www. kanungo. com/, (2010).

[15] Q. Ji, Z. Zhu, P. Lan. Real-time nonintrusive monitoring and prediction of driver fatigue, IEEE Transactions on Vehicular Technology 53 (2004) 1052–1068.

DOI: 10.1109/tvt.2004.830974