Objective
The aim of this study is to describe information acquisition theory, explaining how drivers acquire and represent the information they need.
Background
While questions of what drivers are aware of underlie many questions in driver behavior, existing theories do not directly address how drivers in particular and observers in general acquire visual information. Understanding the mechanisms of information acquisition is necessary to build predictive models of drivers’ representation of the world and can be applied beyond driving to a wide variety of visual tasks.
Method
We describe our theory of information acquisition, looking to questions in driver behavior and results from vision science research that speak to its constituent elements. We focus on the intersection of peripheral vision, visual attention, and eye movement planning and identify how an understanding of these visual mechanisms and processes in the context of information acquisition can inform more complete models of driver knowledge and state.
Results
We set forth our theory of information acquisition, describing the gap in understanding that it fills and how existing questions in this space can be better understood using it.
Conclusion
Information acquisition theory provides a new and powerful way to study, model, and predict what drivers know about the world, reflecting our current understanding of visual mechanisms and enabling new theories, models, and applications.
Application
Using information acquisition theory to understand how drivers acquire, lose, and update their representation of the environment will aid development of driver assistance systems, semiautonomous vehicles, and road safety overall.
The aim of this study is to describe information acquisition theory, explaining how drivers acquire and represent the information they need.
Background
While questions of what drivers are aware of underlie many questions in driver behavior, existing theories do not directly address how drivers in particular and observers in general acquire visual information. Understanding the mechanisms of information acquisition is necessary to build predictive models of drivers’ representation of the world and can be applied beyond driving to a wide variety of visual tasks.
Method
We describe our theory of information acquisition, looking to questions in driver behavior and results from vision science research that speak to its constituent elements. We focus on the intersection of peripheral vision, visual attention, and eye movement planning and identify how an understanding of these visual mechanisms and processes in the context of information acquisition can inform more complete models of driver knowledge and state.
Results
We set forth our theory of information acquisition, describing the gap in understanding that it fills and how existing questions in this space can be better understood using it.
Conclusion
Information acquisition theory provides a new and powerful way to study, model, and predict what drivers know about the world, reflecting our current understanding of visual mechanisms and enabling new theories, models, and applications.
Application
Using information acquisition theory to understand how drivers acquire, lose, and update their representation of the environment will aid development of driver assistance systems, semiautonomous vehicles, and road safety overall.