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.

A knowledge-based decision support system for shipboard damage control

Abstract: The operational complexity of modern ships requires the use of advanced applications, called damage control systems (DCSs), able to assist crew members in the effective handling of dangerous events and accidents. In this article we describe the development of a knowledge-based decision support system (KDSS) integrated within a DCS designed for a national navy. The KDSS uses a hybrid design and runtime knowledge model to assist damage control operators through a kill card function which supports damage identification, action scheduling and system reconfiguration. We report a fire fighting scenario as illustrative application and discuss a preliminary evaluation of benefits allowed by the system in terms of critical performance measures. Our work can support further research aimed to apply expert systems to improve shipboard security and suggest similar applications in other contexts where situational awareness and damage management are crucial.

An Evaluation of the Taxiway Navigation and Situation Awareness (T-NASA) System in High-Fidelity Simulation

Abstract: The effects of an electronic moving map and a HUD on ground taxi performance in reduced visibility were examined in a high-fidelity simulation Sixteen commercial flight crews completed 21 trials, each consisting of an autoland arrival to Chicago O’Hare and taxi to an apron area Relative to a baseline (paper-chart only) condition, the EMM/HUD combination increased forward speed by 21%, and reduced navigation errors by nearly 100% These results, together with workload ratings, situation awareness ratings, analyses of crew interactions, and pilot feedback, provide strong evidence that the combination of head-up symbology and an EMM can substantially improve both the efficiency and the safety of ground operations

Data to decision: pushing situational information needs to the edge of the network

Abstract: Obtaining a high level of situation awareness while maintaining optimal utilization of resources is becoming increasingly important, especially in the context of asymmetric warfare, where information superiority is crucial for maintaining the edge over the opponent. Obtaining an adequate level of situational information from an ISR system is dependent on sensor capabilities as well as the ability to cue the sensors appropriately based on the current information needs and the ability to utilize the collected data with suitable data processing methods. Applying the Data to Decision approach for managing the behavior of sensor systems facilitates optimal use of sensor assets while providing the required level of situational information. The approach presented in the paper combines the Data to Decision approach with the Fog Computing paradigm, where the computation is pushed to the edge of the network. This allows to take advantage of Big Data potentially generated by the sensor systems while keeping the resource requirements in terms of bandwidth manageable. We suggest a System of Systems approach for assembling the ISR system, where individual systems have a high level of autonomy and the computational resources to perform the necessary computation tasks. To facilitate a composition of a System of Systems of sensors for tactical applications the proactive middleware ProWare is applied. The work presented in the paper has been conducted as part of the European Defense Agency project IN4STARS, in the context of which an implementation of a sensor solution is being built, which facilitates on-line sensor cueing and collaboration between sensors by building upon the Fog Computing paradigm and utilizing the Data to Decision concepts.

A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control

Abstract: Master–slave control is a common form of human–robot interaction for robotic surgery. To ensure seamless and intuitive control, a mechanism of self-adaptive motion scaling during teleoperaton is proposed in this letter. The operator can retain precise control when conducting delicate or complex manipulation, while the movement to a remote target is accelerated via adaptive motion scaling. The proposed framework consists of three components: 1) situation awareness, 2) skill level awareness, and 3) task awareness. The self-adaptive motion scaling ratio allows the operators to perform surgical tasks with high efficiency, forgoing the need of frequent clutching and instrument repositioning. The proposed framework has been verified on a da Vinci Research Kit to assess its usability and robustness. An in-house database is constructed for offline model training and parameter estimation, including both the kinematic data obtained from the robot and visual cues captured through the endoscope. Detailed user studies indicate that a suitable motion-scaling ratio can be obtained and adjusted online. The overall performance of the operators in terms of control efficiency and task completion is significantly improved with the proposed framework.