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Some future air traffic management concepts seek to place more separation responsibility on the pilot in order to achieve greater aircraft operating autonomy. Separating one’s own aircraft from others in something other than a see-and-avoid environment, however, would pose fundamentally new demands and challenges for pilots, and it is likely that new automation and display tools would be needed. Ideally, an automated strategic conflict avoidance system would behave consistently with pilot expectations and take pilot interests into account when suggesting resolution strategies. It might also recognize situations that pilots may have difficulty detecting and resolving on their own. At this time, little is known about how pilots perceive airspace complexity in self-separation tasks. In this study, we used a Cockpit Display of Traffic Information (CDTI) with an embedded strategic conflict avoidance aid to help fourteen commercial transport pilots detect and resolve a series of strategic conflict situations. We then assessed their performance with and without the aid, recorded and analyzed pilot ratings of aid effectiveness and usability, and used a neural network model to associate complexity ratings with airspace characteristics to determine which sets of characteristics most heavily influenced pilot perceptions of airspace complexity. The results of this analysis provide insight into what aspects of airspace configuration may have the greatest influence on pilot perceived workload and difficulty understanding conflict situations.