This paper introduces an Artificial Intelligence-based system to support operators to manage the risk of collision. The system is based on the concepts of Belief and Plausibility coming from Dempster-Shaffer's Evidence Theory applied to collision risk assessment. A revised calculation of the Probability of Collision, Pc) is proposed to mitigate the Dilution of Probability that affects the usual definition of this quantity. This phenomenon gives the counterintuitive idea that the lower the quality of the data (or amount of information available to the operators), the smaller the probability of collision. When different sources provide contradictory information, bigger uncertainties are considered which can lead to false confidence in the likelihood of a collision or forces operators to accept very large margins. The method presented here will account for epistemic uncertainty under the assumption of Evidence Theory which leads to the definition of confidence intervals on the probability of a collision. Confidence intervals incorporate the dependency of the probability of collision on the amount and quality of the available information, using the concepts of Belief and Plausibility introduced in Evidence Theory. The result of this revised calculation of the Pc is a more informed decision. At the same time, a lack of information can lead to a higher uncertainty on the decision to be made. Thus the paper will propose a possible approach to make optimal decisions under epistemic uncertainty, considering a given conjunction geometry and the time to the encounter. In addition to this new approach, an Artificial Intelligence-based system is applied to automatically provide the optimal decisions. A virtual database with a set of encounter geometries and associated uncertainties intervals have been created for training and validating the system. A set of Machine Learning techniques has been used to obtain preliminary results on the potential performance of the system. The system is presented under the form of a classification, where each of the classes for an encounter event is a suggested decision for the operator. Two approaches have been proposed. The first of them uses values of Belief and Plausibility at certain Pc thresholds and the time to the encounter for predicting the class. Very accurate results are provided by the techniques tested. The second approach uses the geometry of the encounter, allowing to skip the time-consuming step of computing Belief and Plausibility. Results suggest that Machine Learning techniques can be applied for obtaining an Artificial Intelligence-based system for supporting operators, although improvements on the methods should be done and a systematic analysis comparing techniques is recommended.

中文翻译：

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使用机器学习和证据理论改进碰撞风险管理

本文介绍了一种基于人工智能的系统，以支持操作员管理碰撞风险。该系统基于适用于碰撞风险评估的 Dempster-Shaffer 证据理论的可信度和合理性概念。建议对碰撞概率 (Pc) 进行修订计算，以减轻影响该数量通常定义的概率稀释。这种现象给出了一个违反直觉的想法，即数据的质量（或操作员可用的信息量）越低，碰撞的可能性就越小。当不同来源提供相互矛盾的信息时，会考虑更大的不确定性，这可能导致对碰撞可能性的错误信心或迫使运营商接受非常大的裕度。此处介绍的方法将在证据理论的假设下考虑认知不确定性，从而导致对碰撞概率的置信区间的定义。置信区间结合了碰撞概率对可用信息的数量和质量的依赖性，使用证据理论中引入的置信度和似然性概念。Pc 的这种修正计算的结果是一个更明智的决定。同时，信息的缺乏会导致决策的不确定性更高。因此，本文将提出一种可能的方法，在认知不确定性下做出最佳决策，考虑给定的连接几何和相遇的时间。除了这种新方法，应用基于人工智能的系统来自动提供最佳决策。已经创建了一个具有一组遭遇几何和相关不确定性区间的虚拟数据库，用于训练和验证系统。已使用一组机器学习技术来获得有关系统潜在性能的初步结果。该系统以分类的形式呈现，其中遇到事件的每个类别都是操作员的建议决定。已经提出了两种方法。他们中的第一个使用特定 Pc 阈值下的置信度和合理性值以及相遇时间来预测类别。所测试的技术提供了非常准确的结果。第二种方法使用相遇的几何形状，允许跳过计算可信度和合理性的耗时步骤。结果表明，机器学习技术可用于获得支持操作员的基于人工智能的系统，但应改进方法并推荐系统分析比较技术。