Projects
Human Control Models
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Driver modeling and identification for haptic shared control
Parsimonious and identifiable driver models for online identification and interface adaptation.
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Modeling human use of preview information
Measuring and modeling how humans use preview information of a trajectory to follow for anticipation or feedforward control.
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Modeling perceived motion incongruence
Quantitative data-driven perception modeling that separates effects of different types of cueing errors in different axes.
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Feedforward in manual control
Developing models for how humans rely on feedforward control responses in realistic discrete maneuvering tasks.
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Human multi-channel control with visual and motion feedback
Identifying and modeling how humans use visual and physical motion feedback for control.