Police In-Vehicle Technology Adaptation Based on Officer Cognitive State

Law Enforcement Officers (LEOs) experience high levels of workload and stress while on duty. LEO cognitive state varies under different driving conditions, such as patrol, pursuit, and emergency driving. In-vehicle interfaces further increase LEO workload, as LEOs need to process information from the mobile computer terminal and radio, creating a need for adapting information intake under high workload. To address this need, this study introduces a system designed for real-time adaptation of in-vehicle technology for LEOs based on their cognitive state. The system leverages real-time data to track and manage the workload dynamically. Gaze behavior analysis is utilized to determine attention allocation during various driving scenarios. Sensory outputs are monitored to assess physiological measures of workload. Additionally, vehicle dynamics data provide insights into driving behavior under different driving conditions. Based on the live processing of these variables, the system adjusts the in-vehicle interface and information delivery based on the LEO's cognitive state. The system is intended to optimize the interaction between the officer and in-vehicle technology, resulting in better performance and optimizing workload levels. The proposed system could offer a novel approach to integrate context-aware technology into police vehicles. This approach aims to contribute to the field of live information processing and human-system interaction, and more broadly contributes to the advancement of adaptive technologies in high-stress professional environments.