Air traffic controller workload is a limiting factor in the current air traffic management system. Adaptive support systems have the potential to balance controller workload and gain acceptance as they provide support during times of need. Challenges in the design of adaptive support systems are to decide when and how to trigger support. The goal of this study is to gain empirical insights into these challenges through a human-in-the-loop experiment, featuring a simplified air traffic control environment in which a novel triggering mechanism uses the quality of the controller's decisions to determine when support is needed. The designed system seeks to prevent high workload conditions by providing resolution advisories when the controller exceeds a threshold of 'self-complicating' decisions. Results indicate that the new system is indeed capable of increasing the efficiency and safety compared to full manual control without intervention. More adaptive support, however, increased the frustration of participants, decreased acceptance, and did not result in improved workload ratings. These findings suggest that, unless we can better infer human intent in complex work environments, adaptive support at the level of decision-making is problematic. A potentially more fruitful direction is to provide support at the level of information integration, with full decision-making authority with the human.

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Through smart scheduling and triggering of automation support, adaptive automation has the potential to balance air traffic controller workload. The challenge in the design of adaptive automation systems is to decide how and when the automation should provide support. This paper describes the design of a novel mechanism for adaptively invoking automation support. Whereas most adaptive automation support systems are reactive in that they invoke automation support after controller workload has increased, the aim of the designed mechanism is to proactively trigger automation support prior to workload increases. To do this, the mechanism assesses the quality of air traffic controller's decisions. The designed adaptive automation system has been tested in a human-in-the-loop experiment. Results indicate that the adaptive support helps to increase efficiency and safety as compared to manual control. However, lower triggering thresholds (resulting in more frequent automation intervention) increased the frustration level of participants (as measured with NASA TLX) and decreased acceptance of the support.@en

NextGen and SESAR are re-defining each agent's role in the airspace in terms of autonomy, authority and responsibility. Function allocation is the process of defining authority, i.e., which functions are executed by which agents. This is an essential design decision in creating transformative ATM concepts of operation. This paper presents a computational simulation methodology to assess function allocations in early design phases, before functional prototypes and HITL experiments can be developed. Thus, this method applies the same models of the functions regardless of which agent executes them, so that any observed effects can be isolated to the function allocation without confounds. A case study is presented in which ten potential function allocations within a new concept of operation were evaluated. A distinction is made between coherent and incoherent function allocations. The key metrics of the function allocations include the time history of each agent's task load and required information exchange with other agents. The results show that the coherency of a function allocation can have a pronounced effect on the amount of information requirements. The paper concludes with a discussion of how this method can be applied to other concepts of operation, and how this method can be used, after the early-in-design analysis described here, as an evolving computational analysis tool for more detailed evaluations using higher fidelity models.

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