To improve state-of-the-art fatigue modeling technology to account for individual differences in fatigue and performance.
The Phase I Small Business Innovation Research (SBIR) project established the feasibility of a model-based approach to achieve accurate individualized driver alertness predictions. This was an essential first step in the development of a robust, individualized Fatigue Management Program (FMP) technology for over-the-road trucking operations. Phase I showed that a combination of optimally weighted lane-tracking metrics could be used to individualize a driver alertness model to significantly improve prediction accuracy. This will provide the foundation for Phase II research and development where additional measures related to driver alertness will be integrated into the alertness prediction algorithm.
Individual differences in vulnerability to sleep loss and fatigue from extended work hours and night work are a substantial problem in transportation work schedule development, fatigue risk management strategies, and prediction of performance impairment in real-world operations. This Phase II SBIR project will deliver an individualized FMP technology for trucking operations that incorporates information about sleep/wake history (from actigraphy), driver alertness (from PERCLOS, a device measuring percent eye closure as a validated correlate of performance impairment due to fatigue), and vehicle performance (from lane tracking) to produce individualized predictions of future performance capability. This technology will be accomplished by building on previously completed work in this area that investigates trait individual differences in vulnerability to sleep loss and develops closed-loop, Bayesian and Kalman filter-based adaptive algorithms to individually tailor mathematical models of performance. The individualized alertness model and FMP technology will be tested in field operations and commercialized in the motor carrier industry to predict future performance of a given individual. The model will be used to improve drivers' schedule development and/or to inform an in-truck drowsiness detection and warning system. It could also be uplinked to allow for monitoring and dynamic resource management.
Fatigue models that incorporate individual differences to allow for improved driver scheduling.
May 2012 - Phase II contract was awarded
May 2014 - Project completion
FY 2011 - $375,000
FY 2012 - $375,000
The contractor is developing an Android-based mobile application for administering the psychomotor vigilance test (PVT), daily questionnaires (Visual Analog Scales), and collecting sleep and caffeine history. In addition, the contractor is recruiting and enrolling drivers for the over-the-road trial.
For more information, contact Theresa Hallquist of FMCSA's Research Division at (202) 366-1064 or email@example.com.
Pulsar Informatics, Inc.