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.

Designing Intelligent Robots for Human-Robot Teaming in Urban Search and Rescue

Abstract: The paper describes ongoing integrated research on designing intelligent robots that can assist humans in making a situation assessment during Urban Search & Rescue (USAR) missions. These robots (rover, microcopter) are deployed during the early phases of an emergency response. The aim is to explore those areas of the disaster hotzone which are too dangerous or too difficult for a human to enter at that point. This requires the robots to be " intelligent " in the sense of being capable of various degrees of autonomy in acting and perceiving in the environment. At the same time, their intelligence needs to go beyond mere task-work. Robots and humans are interdependent. Human operators are dependent on these robots to provide information for a situation assessment. And robots are dependent on humans to help them operate (shared control) and perceive (shared assessment) in what are typically highly dynamic, largely unknown environments. Robots and humans need to form a team. The paper describes how various insights from robotics and Artificial Intelligence are combined , to develop new approaches for modeling human robot teaming. These approaches range from new forms of mod-eling situation awareness (to model distributed acting in dynamic space), human robot interaction (to model communication in teams), flexible planning (to model team coordination and joint action), and cognitive system design (to integrate different forms of functionality in a single system).

Intrusion activity projection for cyber situational awareness

Abstract: Previous works in the area of network security have emphasized the creation of intrusion detection systems (IDSs) to flag malicious network traffic and computer usage. Raw IDS data may be correlated and form attack tracks, each of which consists of ordered collections of alerts belonging to a single multi-stage attack. Assessing an attack track in its early stage may reveal the attackerpsilas capability and behavior trends, leading to projections of future intrusion activities. Behavior trends are captured via variable length Markov models (VLMM) without predetermined attack plans. A virtual terrain schema is developed to model network and system configurations, and used to estimate critical elements and vulnerabilities exposed to each attacker given his/her progress. Experimental results show promises for these proactive measures in ensuring continuous and critical cyber operations.

A new DSS based on situation awareness for smart commerce environments

Abstract: Real time adaptation of marketing strategies and actions in smart commerce environments, such as shops and malls, is an open challenge with a tremendous impact for the survival of traditional retailers. A main issue of traditional retailers, in comparison with e-commerce shops, is that they usually rely on analysis of point-of-sales data after purchase and/or focus groups and self-reports where customers are asked about what they like or want. This techniques, even if solid grounded to marketing and consumer research, do not allow analysis of data and decision making in real time, i.e., when consumers are inside a shop. In this paper we present our results on the definition and validation of a decision support system for real time decision making on discount and promotion actions. The system makes decision on the basis of recognition and assessment of situations of interest for the consumers, modelled with heuristics related to behavioural economics results. We validated our solution in a virtual shop simulated with V-REP, demonstrating its capabilities to adapt with regards to the changes in the environments, in terms of sensors, people, products, and different situations.

Identification of Important “Party Line” Information Elements and Implications for Situational Awareness in the Datalink Environment

Abstract: The perceived importance and utilization of “party line” information by air carrier flight crews was investigated through a survey and flight simulation study. The Importance, Availability, and Acc...

Systematic surveillance for UAVs: A feedforward iterative learning control approach

Abstract: UAVs (unmanned air vehicles) have contributed greatly to situational awareness through surveillance missions. However, complete autonomy of a UAV has yet to be realized due to the lack of reliable onboard sensing capabilities. Current research at the Air Force pairs UAVs with Unattended Ground Sensors (UGS) to create a system that autonomously patrols, detects, and isolates intruders on a road network. During the patrol phase a UAV must visit each UGS, transitioning to the isolation phase if an intruder is detected. Optimizing the UAV flight plan during the patrol phase will lead to a faster response time and higher probability of capturing the intruder. The goal of this work is to investigate a learning-based approach that will enable more efficient and effective surveillance operations. In the proposed framework, techniques from adaptive feedforward iterative learning control and a region of attraction-based tracking approach have been used to optimize the UAV flight plan between surveillance flights. The proposed approach has resulted in the development of a novel learning control framework that leverages a region-based tracking requirement to minimize overall distance traveled, while guaranteeing convergence within the required tracking zone. Simulation results for a 1D example system demonstrate the validity of the control framework through an 8% reduction of overall distance traveled as compared to traditional surveillance strategies requiring tracking convergence to a single point. The proposed framework has the potential to significantly decrease the resources required for surveillance-based applications.