  Estimating the probability of failures or accidents with aerospace systems is
often necessary when new concepts or designs are introduced, as it is being
done for Autonomous Aircraft. If the design is safe, as it is supposed to be,
accident cases are hard to find. Such analysis needs some variance reduction
technique and several algorithms exist for that, however specific model
features may cause difficulties in practice, such as the case of system models
where independent agents have to autonomously accomplish missions within finite
time, and likely with the presence of human agents. For handling these
scenarios, this paper presents a novel estimation approach, based on the
combination of the well-established variation reduction technique of
Interacting Particles System (IPS) with the long-standing optimization
algorithm denominated DIviding RECTangles (DIRECT). When combined, these two
techniques yield statistically significant results for extremely low
probabilities. In addition, this novel approach allows the identification of
intermediate events and simplifies the evaluation of sensitivity of the
estimated probabilities to certain system parameters.
