Project Details


The objective of this project is to enable the improved design of human-machine systems that use virtual and mixed-reality (i.e., 'immersive') environments. Immersive environments are used broadly, with applications from entertainment to design. They provide a safe training environment for high-risk scenarios faced by soldiers, pilots, and surgeons. They are also used to assess potentially risky behaviors in human-machine systems (e.g., distracted driving) and potential interventions (e.g., collision avoidance, surgical planning). As immersion varies from purely virtual to purely real, the behavior of the human-machine system changes, particularly when the designed artifact exposes humans to risk. Design decisions focused on preventing human danger are made by using risk-free virtual interactions. As a result, risk compensation and other effects can confound the results of design assessments, training, and remote operator performance. This effort will identify and model these effects, providing guidance to engineers and designers working with immersive technologies.

This work leverages advances in simulator hardware and data rendering to deeply investigate behavioral changes due to perceived risk within digitally-mediated man-machine systems. To isolate and investigate these behavioral changes, three research objectives are identified. First, determine the degree to which simulated and augmented reality environments are comparable surrogates for predicting risk in live, in-use behaviors. Second, identify the biases in risk perception between different immersion modalities and boundaries where biases are so large that simulations become deficient in evaluating risky behaviors. Third, quantify variance in risk compensation behavior. Collectively, this effort will correlate the effects of using digitally-augmented and/or virtual environments to behavioral changes, with the goal of understanding how immersion changes decision making and performance.

Effective start/end date7/1/166/30/19


  • National Science Foundation: $408,072.00


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