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

Abstract:

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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.

Info:

Periodical:

Key Engineering Materials (Volumes 467-469)

Edited by:

Dehuai Zeng

Pages:

23-30

DOI:

10.4028/www.scientific.net/KEM.467-469.23

Citation:

Q. Wu and W. W. Yu, "Integrating a HMM-Based Driver Fatigue Recognition Model into Smart Vehicle Space for Ubiquitous Computing", Key Engineering Materials, Vols. 467-469, pp. 23-30, 2011

Online since:

February 2011

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Price:

$35.00

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