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 in aviation systems. in: handbook of aviation human factors

Abstract: In the aviation domain, maintaining a high level of situation awareness is one of the most critical and challenging features of an aircrew's job. Situation awareness (SA) can be thought of as an internalized mental model of the current state of the flight environment. SA is formally defined as "the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the future". SA can be achieved by drawing on a number of internal mechanisms. The degree to which these structures can be developed and effectively used in the flight environment, the degree to which aircrew can effectively deploy goal driven processing in conjunction with data driven processing, and the degree to which aircrew can avoid the hazards of automaticity will ultimately determine the quality of their SA.

Single operator, multiple robots: an eye movement based theoretic model of operator situation awareness

Abstract: For a single operator to effectively control multiple robots, operator situation awareness is a critical component of the human-robot system. There are three levels of situation awareness: perception, comprehension, and projection into the future [1]. We focus on the perception level to develop a theoretic model of the perceptual-cognitive processes underlying situation awareness. Eye movement measures were developed as indicators of cognitive processing and these measures were used to account for operator situation awareness on a supervisory control task. The eye movement based model emphasizes the importance of visual scanning and attention allocation as the cognitive processes that lead to operator situation awareness and the model lays the groundwork for real-time prediction of operator situation awareness.

Adaptive Automation in a Naval Combat Management System

Abstract: There is a continuing trend of letting fewer people deal with larger amounts of information in more complex situations using highly automated systems. In such circumstances, there is a risk that people are overwhelmed by information during intense periods or, on the other hand, do not build sufficient situational awareness during periods of slack to deal with situations where human intervention becomes necessary. A number of studies show encouraging results in increasing the efficiency of human-machine systems by making the automation adapt itself to the human needs. Current literature shows no examples of adaptive automation in real operational settings, however. We introduce a fine-grained adaptation methodology based on well-established concepts that is easy to comprehend and likely to be accepted by the end user. At the same time, we let the machine operate like a virtual team member in that it continuously builds its own view of the situation independent from the human. Working agreements between human and machine provide lower and upper bounds of automation that are in advance determined by the end user so that unwanted appropriation of responsibility by the machine is avoided. The framework is domain neutral and therefore thought to be applicable across a wide range of complex systems, both military and civilian. It gives researchers an architecture that they can use in their own work to get adaptive automation up and running quickly and easily.

Providing Information on the Spot: Using Augmented Reality for Situational Awareness in the Security Domain

Abstract: For operational units in the security domain that work together in teams, it is important to quickly and adequately exchange context-related information to ensure well-working collaboration. Currently, most information exchange is based on oral communication. This paper reports on different scenarios from the security domain in which augmented reality (AR) techniques are used to support such information exchange. The scenarios have been designed with a User Centred Design approach, in order to make the scenarios as realistic as possible. To support these scenarios, an AR system has been developed and evaluated in two rounds. In the first round, the usability and feasibility of the AR support has been evaluated with experts from different operational units in the security domain. The second evaluation round then focussed on the effect of AR on collaboration and situational awareness within the expert teams. With regard to the usability and feasibility of AR, the evaluation shows that the scenarios are well defined and the AR system can successfully support information exchange in teams operating in the security domain. The second evaluation round showed that AR can especially improve the situational awareness of remote colleagues not physically present at a scene.

Situational Awareness in Driving

Abstract: This chapter focuses on three questions. First, what are the attentional component processes that drivers use to maintain situation awareness (SA)? Second, how can drivers’ ability to maintain SA, especially using driving simulators be measured? Third, what is the empirical evidence, especially using simulators, that the particular component processes described here are key parts of maintaining SA and that these components affect driving performance? SA is defined here as the updated, meaningful knowledge of a changing, multifaceted situation that drivers use to guide choice and action. Regarding the first question, it is proposed in this chapter that SA involves component processes of focal vision (including attention allocation within tasks, event comprehension, and task management across concurrent tasks) as well as ambient vision processes (including attention capture by sudden peripheral events). SA is a complex process that requires assessment by a variety of online (during driving) and offline (post-driving) measures. Driving simulators are used in many SA measures. Research using these measures shows that most of the above components of SA can be trained, improve with driving experience, and correlate positively with safe driving.