A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

Abstract

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.

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