Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Adaptive Control of Systems with Actuator and Sensor Nonlinearities
Adaptive Control of Systems with Actuator and Sensor Nonlinearities
Introduction to Human Factors Engineering (2nd Edition)
Introduction to Human Factors Engineering (2nd Edition)
Identification of driver state for lane-keeping tasks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A probabilistic framework for modeling and real-time monitoring human fatigue
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Spatiotemporal-boosted DCT features for head and face gesture analysis
HBU'10 Proceedings of the First international conference on Human behavior understanding
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Robust classification of face and head gestures in video
Image and Vision Computing
Detecting driver drowsiness using feature-level fusion and user-specific classification
Expert Systems with Applications: An International Journal
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This paper aims to provide reliable indications of driver drowsiness based on the characteristics of driver-vehicle interaction. A test bed was built under a simulated driving environment, and a total of 12 subjects participated in two experiment sessions requiring different levels of sleep (partial sleep-deprivation versus no sleep-deprivation) before the experiment. The performance of the subjects was analyzed in a series of stimulus-response and routine driving tasks, which revealed the performance differences of drivers under different sleep-deprivation levels. The experiments further demonstrated that sleep deprivation had greater effect on rule-based than on skill-based cognitive functions: when drivers were sleep-deprived, their performance of responding to unexpected disturbances degraded, while they were robust enough to continue the routine driving tasks such as lane tracking, vehicle following, and lane changing. In addition, we presented both qualitative and quantitative guidelines for designing drowsy-driver detection systems in a probabilistic framework based on the paradigm of Bayesian networks. Temporal aspects of drowsiness and individual differences of subjects were addressed in the framework.