Situation awareness

About: Situation awareness is a research topic. Over the lifetime, 7380 publications have been published within this topic receiving 108695 citations. The topic is also known as: SA & situational awareness.

Situation Awareness for Socio Technical Systems : A simulation gaming study in intermodal transport operations

Abstract: Operating socio technical systems such as energy distribution networks, power plants, container terminals, and healthcare systems is a grand challenge. Decision making in these systems is complex due to their size, diversity, dynamism, social component, distributed nature, uncertainty, and vulnerability to disruptions. Human actors in these systems have to channel their pre-decision time to assess and classify current situation based on their individual or organizational goals rather than analyse possible alternatives for an optimal outcome. In this effect, Situation Awareness, a human factor required to perceive, comprehend and project the future of a current situation is considered to be an essential prerequisite for decision making in socio technical systems. Although the importance of Situation Awareness is well established it has not been studied extensively in socio technical systems. Therefore the key objective of this dissertation was to study the role of Situation Awareness on decision making and performance of individuals and teams in socio technical systems within the context of intermodal transport operations in container terminals.

Disruptions, Interruptions and Information Attack: Impact on Situation Awareness and Decision Making

Abstract: This paper presents a model of depicting the ways in which disruptions, interruptions and information attack can effect situation awareness and decision making in a variety of contexts. Those effected in military aviation include not just those in the cockpit, but also forward air controllers, ground based air traffic controllers and those in intelligence and support functions. The model incorporates the ways in which information attacks can effectively disrupt human decision making at various points in information processing. By carefully examining not just what cues might depict an attack to information systems, but also how human observers will be effected by such cues, more robust systems for protecting against disruptions and information attack can be developed.

Situational Assessment for Intelligent Vehicles Based on Stochastic Model and Gaussian Distributions in Typical Traffic Scenarios

Abstract: In intelligent driving, situational assessment (SA) is an important technology, which helps to improve the cognitive ability of intelligent vehicles in the environment. Uncertainty analysis is very significant in situation assessment. This article proposes an SA method based on uncertainty risk analysis. Under uncertain conditions, according to the random environment model and Gaussian distribution model, the collision probability between multiple vehicles is estimated by comprehensive trajectory prediction. The proposed method considers collision probabilities of different prediction points within and outside the prediction range and obtains long-term accurate prediction results. The method is suitable for the situation risk assessment of sensor systems in the presence of unexpected dynamic obstacles, sensor failures or communication losses in traffic, and different environmental sensing accuracy. The experimental results show that in the dynamic traffic environment, the proposed scenario assessment method can not only accurately predict and assess the situation risks within the prediction range, but also provide accurate scenario risk assessment outside the prediction range.

Multi-Entity Bayesian Networks Learning for Predictive Situation Awareness

Abstract: : Over the past two decades, machine learning has led to substantial changes in Data Fusion Systems globally One of the most important application areas for data fusion is situation awareness to support command and control Situation awareness is perception of elements in the environment, comprehension of the current situation, and projection of future status before decision making Traditional fusion systems focus on lower levels of the JDL hierarchy, leaving higher-level fusion and situation awareness largely to unaided human judgment This becomes untenable in today's increasingly data-rich environments, characterized by information and cognitive overload Higher-level fusion to support situation awareness requires semantically rich representations amenable to automated processing Ontologies are an essential tool for representing domain semantics and expressing information about entities and relationships in the domain Probabilistic ontologies augment standard ontologies with support for uncertainty management, which is essential for higher-level fusion to support situation awareness PROGNOS is a prototype Predictive Situation Awareness (PSAW) System for the maritime domain The core logic for the PROGNOS probabilistic ontologies is Multi-Entity Bayesian Networks (MEBN), which combine First-Order Logic with Bayesian Networks for representing and reasoning about uncertainty in complex, knowledge-rich domains MEBN goes beyond standard Bayesian networks to enable reasoning about an unknown number of entities interacting with each other in various types of relationships, a key requirement for PSAW The existing probabilistic ontology for PROGNOS was constructed manually by a domain expert However, manual MEBN modeling is labor-intensive and not agile We have developed a learning algorithm for MEBN-based probabilistic ontologies This paper presents a bridge between MEBN and the Relational Model, and a parameter and structure learning algorithm for MEBN