Harnessing Existing Technologies to Mitigate Driving Distraction Among Law Enforcement Officers

A majority of law enforcement officers (LEOs) killed in the line of duty are due to automobile crashes and distraction is a common causal factor in these crashes. Officers in patrol vehicles need to interact with a whole array of devices in their vehicle, while patrolling. Extant commercial systems do not control all the systems due to interoperability and proprietary constraints. Furthermore, they have not been designed with explicit intention of mitigating officer distraction.

To address this challenge the University of Wisconsin-Madison (UW) has assembled a transdisciplinary team of experts in traffic safety, human factors, human-computer interaction and law enforcement officers (LEOs) to: 1. Perform an objective and quantitative evaluation of tasks officers engage in while driving, 2. Identify appropriate interventions and 3. Develop and demonstrate a system that harnesses the power of existing technologies to reduce visual/manual/cognitive distraction for officers while driving. UW proposes a two-phase research project.

Phase 1 will use focus group discussions with LEOs across the country to identify the needs of officers to interact with different in-vehicle systems/software and also the characteristics of the software/system. Contextual inquiry and task analysis, will follow focus group discussions, and will result in task hierarchies, information flow diagrams, and task sequence diagrams to identify a broad range of methods to mitigate driver distraction.

Phase 2 will evaluate potential interventions using driving simulator experiments and field trials after obtaining IRB approval. Up to 60 LEOs will participate in the simulator experiments. In addition to Wisconsin State Patrol and Sheboygan County Sheriff that have committed to participate in the focus group discussions, driving simulator experiments and field trials, UW team will reach out to agencies across the country. Measures of performance will include correctness and timeliness of work-related tasks, cognitive workload using the Tactile Detection Response Task (TDRT) and visual distraction using off-road glances. Data will be analyzed using a mixed effects logistic regression model for probability of TDRT detection, and a mixed effects regression model for TDRT response time, eye glance metrics, driving and work task performance.

Finally, a benefit-cost analysis will be performed and the entire research will be documented in a final report. All the data obtained will be anonymized, archived and made available to others. In addition, any software tools or technologies developed by the research team will be made available under GNU public license.

"Note: This project contains a research and/or development component, as defined in applicable law," and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14).

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