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There is a large amount of variation between novices and experts in their cognitive workload when performing tasks. A naturalistic pilot study was conducted with nine novice law enforcement officers (nLEOs) to determine how their use of in-vehicle technology affected their cognitive workload during their normal patrols. Physiological data were collected using a novel synchronization process for naturalistic driving studies, allowing heart rate variability and eye tracking measurements to be synchronized together and directly compared to subjective workload levels. It was found that nLEOs have average or higher workload compared to experienced officers and the general population when they are on duty. Future studies can utilize the approaches and findings of this pilot study for conducting naturalistic driving studies and developing cognitive performance models for novice users.more » « less
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Objective This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology.
Background HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored.
Method A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method.
Results The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs.
Conclusion The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables.
Application The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.
