Estimating driving performance based on EEG spectrum analysis
EURASIP Journal on Applied Signal Processing
Engineering Applications of Artificial Intelligence
Investigation of the conflict between the driver and a vehicle steering assist controller
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Coordination of the authority between the vehicle driver and a steering assist controller
WSEAS Transactions on Systems and Control
Detection of driver fatigue caused by sleep deprivation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Real-time driving behavior identification based on driver-in-the-loop vehicle dynamics and control
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Symbolic modeling of driving behavior based on hierarchical segmentation and formal grammar
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Engineering Applications of Artificial Intelligence
IEEE Transactions on Intelligent Transportation Systems
A situation-adaptive lane-keeping support system: overview of the SAFELANE approach
IEEE Transactions on Intelligent Transportation Systems
Distributed sensor for steering wheel grip force measurement in driver fatigue detection
Proceedings of the Conference on Design, Automation and Test in Europe
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Identification of driver state is a desirable element of many proposed vehicle active safety systems (e.g., collision detection and avoidance, automated highway, and road departure warning systems). In the paper, driver state assessment is considered in the context of a road departure warning and intervention system. A system identification approach, using vehicle lateral position as the input and steering wheel position as the output, is used to develop a model and to update its parameters during driving. Preliminary driving simulator results indicate that changes in the bandwidth and/or parameters of such a model may be useful indicators of driver fatigue. The approach is then applied to data from 12 2-h highway driving runs conducted in a full-vehicle driving simulator. The identified model parameters (ζ ωn , and DC gain) do not exhibit the trends expected as lane keeping performance deteriorates, despite having acceptably white residuals. As an alternative, model residuals are compared in a process monitoring approach using a model fit to an early portion of the 2-h driver run. Model residuals show the expected trends and have potential in serving as the basis for a driver state monitor