Estimation of lane marker parameters with high correlation to steering signal

Human behavior models give insight into people's choices and actions and are tools for predicting performance and improving interface design. Most models focus on a task's cognitive aspects or its physical requirements. This research addresses the divide between cognitive and physical models by combining two models to produce an integrated cognitive-physical human model that enables studying of complex human-machine interactions. The capabilities of the integrated model are evaluated in a task scenario with both cognitive and physical components, i.e., driving while performing a secondary in-vehicle task. When applied in this way, the integrated model is called the Virtual Driver model and can replicate basic driving, in-vehicle tasks, and resource-sharing behaviors, providing a new way to study driver distraction. The model has applicability to interface design and predicting staffing requirements and performance.