L. C. Botega, Gustavo Marttos Cáceres Pereira, Valdir Amancio Pereira Junior, Allan Oliveira
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引用次数: 0
摘要
态势感知(SAW)是一个在需要做出关键决策的领域非常普遍的概念,指的是个人或团队对某一态势的意识水平。糟糕的态势感知会导致人类在决策过程中出现失误,造成生命和财产损失。数据融合过程通过整合来自不同来源的异构协同数据,并将其转化为更有意义的决策支持,为丰富有关情况的知识提供了机会。然而,当信息存在质量问题时,尤其是当人类是数据的主要来源时,问题就出现了。这项工作描述了对紧急情况评估系统(ESAS)提供的情况认识的评估,该系统是根据新的信息融合模型的原则建立的。来自圣保罗州警察局(PMESP)的专家使用 SART 方法(态势感知评级技术)对 ESAS 进行了评估,结果显示,与另一个基于最先进的高级融合模型的系统相比,ESAS 的态势感知率更高,尤其是在与信息供应和态势感知相关的问题上。
Assessing Situation Awareness on Fusion-Driven Emergency Management Systems
Situational Awareness (SAW) is a widespread concept in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. A poor SAW can induce humans to failures in the decision-making process, leading to losses of lives and property damage. Data fusion processes present opportunities to enrich the knowledge about situations by integrating heterogeneous and synergistic data from different sources and transforming them into more meaningful subsidies for decision-making. However, a problem arises when information is subject to problems concerning its quality, especially when humans are the main sources of data (HUMINT). This work describes the assessment of situation awareness provided by an emergency situation assessment system (ESAS), build based on the principles of a new information fusion model. Experts from the São Paulo State Police (PMESP) evaluated ESAS using SART methodology (Situation Awareness Rating Technique), which showed higher rates of SAW, compared to another system, based on the state-of-the-art high-level fusion model, especially in questions relating to the components of informational supply and situational understanding.