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Runway incursions are low probability events resulting from complex combinations of cognitive and environmental factors, which can have deadly consequences. However, the development and evaluation of tools to reduce runway incursions are, ironically, hampered by the low incidence of such events. A possible path forward is the use of high-fidelity cognitive models to predict pilot performance under a wide variety of airport conditions and runway circumstances. We describe a fully embodied ACT-R 6.0 cognitive model, named SimPilot, of a pilot taxiing a simulated Boeing 737-800 aircraft. The goals of SimPilot are twofold. The first is automated testing of a new safety devices. The second goal is to show that modeling the multitasking inherent to taxiing in a cognitive plausible manner is an important step in predicting and preventing runway incursions.