Efficient estimation of probability of conflict between air traffic using Subset Simulation

TL;DR

This paper introduces an efficient Subset Simulation method to accurately estimate low probabilities of air traffic conflicts, significantly reducing computational effort compared to traditional Monte Carlo methods.

Contribution

The paper applies Subset Simulation to air traffic conflict probability estimation, improving efficiency and accuracy over naive Monte Carlo approaches.

Findings

Subset Simulation reduces computational load by one or more orders of magnitude.

The method accurately estimates low conflict probabilities in various scenarios.

Demonstrates practical utility for autonomous Sense-and-Avoid systems.

Abstract

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.