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.

Exploring camera viewpoint control models for a multi-tasking setting in teleoperation

Abstract: Control of camera viewpoint plays a vital role in many teleoperation activities, as watching live video streams is still the fundamental way for operators to obtain situational awareness from remote environments. Motivated by a real-world industrial setting in mining teleoperation, we explore several possible solutions to resolve a common multi-tasking situation where an operator is required to control a robot and simultaneously perform remote camera operation. Conventional control interfaces are predominantly used in such teleoperation settings, but could overload an operator's hand-operation capability, and require frequent attention switches and thus could decrease productivity. We report on an empirical user study in a model multi-tasking teleoperation setting where the user has a main task which requires their attention. We compare three different camera viewpoint control models: (1) dual manual control, (2) natural interaction (combining eye gaze and head motion) and (3) autonomous tracking. The results indicate the advantages of using the natural interaction model, while the manual control model performed the worst.

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Abstract: The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Abstract: Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remote-sensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.

Fundamental issues in systematic design of airborne networks for aviation

Abstract: Airborne networks are special types of ad hoc wireless networks that can be used to enhance situational awareness, flight coordination, and flight efficiency in civil aviation. A unique challenge to the proper design of airborne networks for these applications is the dynamic coupling between airborne networks and flight vehicles. This coupling directly affects the operations and performances of both the networks and the vehicles. Accordingly, an appropriate method of airborne network design must consider its interactions with vehicle flight maneuvers and vice versa. In this paper, fundamental issues in the systematic design of airborne networks for civil aviation are addressed. We particularly focus on the issues that cover various aspects of the design such as establishing the airborne network, maintaining the network, and evaluating the performances of the network.

Analysis of Operational Errors and Workload in Air Traffic Control

Abstract: The Federal Aviation Administration has embarked on a major curriculum redesign effort to improve the training of en route air traffic controllers. Included in this effort was a cognitive task analysis. One component of the task analysis was an analysis of operational errors, to obtain insights into cognitive-perceptual factors contributing to controller decisionmaking error. The data suggest that a failure to maintain situation awareness is the primary cause of controller error. These results highlight the importance of the controller task “maintain situation awareness”, and are consistent with the findings of the other analyses. An approach for training situation awareness skills is presented in relation to models of expertise developed from other analyses: an expert mental model of air traffic control, and a task decomposition listing thirteen primary controller tasks. The findings and training paridigm have implications for training other complex high-performance tasks performed in a real-time, multi-...