  New methodologies will be needed to ensure the airspace remains safe and
efficient as traffic densities rise to accommodate new unmanned operations.
This paper explores how unmanned free-flight traffic may operate in dense
airspace. We develop and analyze autonomous collision avoidance systems for
aircraft operating in dense airspace where traditional collision avoidance
systems fail. We propose a metric for quantifying the decision burden on a
collision avoidance system as well as a metric for measuring the impact of the
collision avoidance system on airspace. We use deep reinforcement learning to
compute corrections for an existing collision avoidance approach to account for
dense airspace. The results show that a corrected collision avoidance system
can operate more efficiently than traditional methods in dense airspace while
maintaining high levels of safety.
