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As autonomous features become pervasive, control strategy research continues. Levels of Autonomy (LoA) provide a method for describing function allocation between operators and autonomous system elements. Unfortunately, LoA does not provide the user interface designer a clear method to distinguish among interface concepts which impose varying levels of operator workload or result in predictable human or system performance changes. This limitation arises as LoA does not distinguish functions which impose significant verses insignificant human workload. For example, a car with autonomous emergency breaking performs breaking at the highest Level of Autonomy. However, this function does not affect the primary decisions made by an automobile driver and automating this function alleviates little, if any, human workload. The current research suggests an alternate classification scheme, specifically Level of Human Control Abstraction (LHCA). LHCA describes how an operator controls a system based on the control tasks performed and the level of decisions made by the operator verses the system. This thesis will discuss five levels within this framework: Direct Control, Augmented Control, Parametric Control, Goal Oriented Control, and Mission Capable Control. Real world and hypothetical systems can be categorized within this framework, potentially providing a framework that is directly related to human workload and performance.