Abstract Since the introduction of automation systems in commercial aircraft, flight safety has improved significantly. However, pilots’ increasing reliance on these systems has led to a decline in their manual flying skills. When automated systems display unexpected behaviors, pilots experience a substantial increase in workload, which may lead to additional unforeseen automation surprises and pose serious risks to flight safety. This study explores the mechanisms underlying automation surprises by mining aviation accident databases and constructing a Bayesian network model. Based on this model, the key factors contributing to automation surprises are further explored, and a human-machine interaction model for commercial aircraft is developed. The findings indicate that building an interaction model between pilots and automated systems helps pilots develop better automation safety awareness. Moreover, “human-centered artificial intelligence” can assist in mitigating safety issues in human-machine interactions from a system design perspective.
Du et al. (Tue,) studied this question.
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