Motor vehicle crashes are a leading cause of line-of-duty deaths for law enforcement officers, accounting for almost 40% of officers' fatal work injuries. They are also 2.5 times more frequent than the national average among all occupations. The main contributors to these crashes include officers' use of in-vehicle technologies while driving, fatigue, and lack of sufficient training in handling high-demand driving situations that arise in law enforcement. This project will model officers' driving workload and performance in high-demand situations, then use those models to develop in-vehicle technology and training solutions that adapt to officers' workload in order to reduce the risk of crash-related harms in police operations. The models, methods, and tools developed may also benefit other driving and training domains. The work will support the research training of graduate and undergraduate students; further, the project team will develop outreach materials for both law enforcement groups and K-12 teachers to help them use the work to support training and STEM education.

The technical aims of the project are divided into three thrusts. Thrust 1 focuses on modeling novice police officers' cognitive, perceptual, and motor demands while driving, using the findings of a naturalistic driving study, knowledge elicitation methods, and cognitive modeling software. Thrust 2 will develop and evaluate adaptive in-vehicle technology interfaces based on a hybrid algorithm that leverages computational cognitive performance models, machine-learning algorithms, and real-time behavioral and physiological measures to improve officers' driving performance and reduce mental workload and distraction. Thrust 3 will develop adaptive driving simulation-based training based on officers' cognitive state and real-time and offline performance measures, assessing whether the adaptive driving simulation-based training is more effective than prior adaptive training protocols based on operational driving performance or visual attention in improving driving, secondary task performance, and learning.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Dan Cosley
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Texas A&M Engineering Experiment Station
College Station
United States
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