  This paper presents an efficient method for estimating the probability of
conflict between air traffic within a block of airspace. Autonomous
Sense-and-Avoid is an essential safety feature to enable Unmanned Air Systems
to operate alongside other (manned or unmanned) air traffic. The ability to
estimate probability of conflict between traffic is an essential part of
Sense-and-Avoid. Such probabilities are typically very low. Evaluating low
probabilities using naive Direct Monte Carlo generates a significant
computational load. This paper applies a technique called Subset Simulation.
The small failure probabilities are computed as a product of larger conditional
failure probabilities, reducing the computational load whilst improving the
accuracy of the probability estimates. The reduction in the number of samples
required can be one or more orders of magnitude. The utility of the approach is
demonstrated by modeling a series of conflicting and potentially conflicting
scenarios based on the standard Rules of the Air.
