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 the 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 while 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.

中文翻译：

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使用子集模拟有效估计空中交通之间的冲突概率

本文提出了一种有效的方法来估计一个空域内空中交通之间发生冲突的概率。自主感知和规避是一项必不可少的安全功能，可让无人空中系统与其他（有人或无人）空中交通一起运行。估计交通之间发生冲突的可能性的能力是感知和避免的重要组成部分。这种概率通常非常低。使用朴素的直接蒙特卡罗评估低概率会产生大量的计算负载。本文应用了一种称为子集模拟的技术。小故障概率被计算为较大条件故障概率的乘积，减少了计算负载，同时提高了概率估计的准确性。所需样本数量的减少可以是一个或多个数量级。该方法的实用性通过基于标准空中规则对一系列冲突和潜在冲突场景进行建模来证明。