Providing appropriate situation awareness within a mixed-initiative control system

Observing the progress in the technology of unmanned combat aerial vehicles (UCAVs) than it can be foreseen that in the future the role of manned combat aircraft will be taken over more and more by unmanned systems. The abandonment of pilots allows for more freedom in the aerodynamic design of the vehicle in regard to weight and acceleration, however, new stealth constraints have a severe impact. The design of UCAV configurations is driven by the special requirements of upcoming missions, as for instance the capabilities of long endurance fl
\nights joined with a low observability. The new demands have a crucial impact on the aerodynamic shape, and, hence, require new solutions for maneuver con-
\ntrol in respect to integration of engine in- and outlets, actuators and other devices. Additionally the new capabilities of long endurance 
\nights has to be joined with low observability. In particular UCAVs are suited to the exploitation of non-conventional control technologies, such as aerodynamic morphing, 
\nflow control, or thrust vectoring. This paper presents a numerical investigation of innovative morphing technologies for aircraft applications and explores the feasibility for such technologies to enhance the maneuverability of unmanned combat aerial vehicles. In Aerodynamics the morphing is understood as a smooth, continuous change in geometry of the outer surface, e.g. the twist of complete wing can be changed in order control role moments. In the considered case morphing is used to provide additional 
\now control mechanisms taking into account the constraint of fllow radar signature. In particular morphing can be used to change the geometry of the airfoil leading edge to control the 
\now around the wings in order to generate additional lift or induce role moments. This paper is concerned with the investigation of the feasibility and effectiveness of morphing devices for the aerodynamic control of UCAVs. Focal point of this work is the morphing of leading edges in order to generate additional lift or role moments for UCAVs 
\nflying in high angle of attack mode. Therefore, the objective of the numerical investigations is the evaluation of the potential of morphing leading edges especially for the enhancement of the maneuverability of Delta- and Lambda-wing configurations with their vortex-dominated 
\now field. Of special interest is 
\now control at high angles of attack (AoA) by targeting an minimized shape adaptation in order to generate a needed additional lift, to induce role moments or to reduce the risk of the potential deleterious effects of vortex breakdown. The morphing of the leading edge must not confused with classical Kruger leading edge 
\nflaps. Although very effective in regard to the increase of lift such fl
\naps can't be used for UCAVs since gaps between fl
\naps and the wing have to be avoided in order to fulll the hard conditions of radar camouflage
\n.
\nThe presented work is part of the DLR Project FaUSST which is the successor of the DLRProject UCAV-2010. The UCAV-2010 project was set up to identify and assess UCAV relevant technologies. The investigations have covered the pre-design process with fast,
\nlow fidelity methods as well as the detailed examination using high fidelity methods like URANS simulations. The verification of the promissing technologies and tools have been done by virtual and experimental prototypes. The combined research has lead to a Lambda wing type UCAV configuration with a medium sweep of the leading edge. The outcoming
\nDLR-F17 conguration has been mainly derived from the so called Saccon geometry developed by EADS-MAS for the RTO/AVT-161 task group.
\nSince the capability of medium to high AoA maneuverability will be investigated the angle of attack has been varied from 6 to 16 degrees.
\nAfter presentation of the technical part, the results of leading edge manipulation are discussed. One focal point is the comparison of the impact of differently morphed leading edge shapes on the fl
\night performance at specific 
\nflight situations. The aerodynamical coefficients, in particular Cl, Cd and Cm, are related to morphing parameters of the changed shape of the leading edges. Another key aspect is the varied effective angle of attack of
\nthe middle part of the wings, see Figure 2. In case of a basic high angle of attack the effective AoA (due to morphing) can change the vortical 
\nflow above the deformed wings enormously, even when the geometrical angle of the airfoil has been morphed only slightly.
\nThis work discusses the effectiveness of counter-rotating morphing of leading edges, since the requirements in regard to radar signature do not allow large geometry changes. This point reduces the degree of freedom of any shape morphing drastically. Thereto, only proper combinations of left and associated right wing deformations have been analyzed and discussed. After the discussion of the most interesting deformations an evaluation of the presented test cases in regard to the maneuverability enhancement of UCAVs concludes the presentation.

With the development of unmanned combat air vehicles (UCAVs) and artificial intelligence (AI), within visual range (WVR) air combat confrontations utilizing intelligent UCAVs are expected to be widely used in future air combats. As controlling highly dynamic and uncertain WVR air combats from the ground stations of the UCAV is not feasible, it is necessary to develop an algorithm that can generate highly intelligent air combat strategies in order to enable UCAV to independently complete air combat missions. In this paper, a 1-vs.-1 WVR air combat strategy generation algorithm is proposed using the multi-agent deep deterministic policy gradient (MADDPG). A 1-vs.-1 WVR air combat is modeled as a two-player zero-sum Markov game (ZSMG). A method for predicting the position of the target is introduced into the model in order to enable the UCAV to predict the target’s actions and position. Moreover, to ensure that the UCAV is not limited by the constraints of the basic fighter maneuver (BFM) library, the action space is considered to be a continuous one. At the same time, a potential-based reward shaping method is proposed in order to improve the efficiency of the air combat strategy generation algorithm. Finally, the efficiency of the air combat strategy generation algorithm and the intelligence level of the resulting strategy is verified through simulation experiments. The results show that an air combat strategy using target position prediction is superior to the one that does not use target position prediction.

Situational Awareness (SAW) is a concept widely used in areas that require critical decision making, such as in the field of emergency management. SAW is related to the level of perception and understanding that an individual has about real events occurring in complex scenarios, which must be managed by critical systems. Such critical systems require specialized user interfaces (UI) to give operators a dynamic understanding of what is happening in the environment. A challenging issue in the design of SAW-oriented interfaces is to determine how the human-computer interface process can be constructed for SAW enrichment, considering environments with heterogeneous data sources, limitations in data quality, and ever-changing situations. The problem increases when information is subject to uncertainties, which may compromise the situation analysis process. In addition, humans make decisions based on their own understanding of the event, which allied with experience and knowledge can be valuable assets to be used to process situational information about emergencies for the acquisition of SAW. The objective of this work is to demonstrate how to include a SAI-oriented UI in the process of evaluating emergency situations and to present the development of a UI that promotes the management of situational information of emergencies to promote the acquisition of SAW. The results present a specified routine for employing specialized UIs in SAW as part of a situation assessment process, which supports a strong integration between the human operator and other phases of the process, such as quality assessment, data fusion and information visualization , As well as a prototype interface that meets the process. A case study with a critical scenario of a theft event is also presented to demonstrate the applicability of the proposed approach

Situational Awareness (SAW) is the ability of a human to perceive, understand and project the states of assets in a real-world environment. In the emergency management domain, SAW is developed by means of a range of information collected from several sources, typically in real time. With this amount of information to be observed by the human operator, the development of SAW is a very complex and dynamic task. To assist this process of developing SAW, several technological and informational approaches have been developed to support decision-making systems, bringing together situational assessment routines (e.g., acquisition, processing and presentation) for the human operator to build SAW based on the information at hand, which can be very quality-limited in many aspects, depending on their source. Since such systems employ concepts and techniques of data and information fusion to perform information integration, issues regarding synergistic information association emerge. In order to enhance and improve this process, it is proposed in this paper a new fusion approach using the concepts of data quality and semantics.

The increasing popularity of Artificial Intelligence (AI) emphasizes the need for a deeper understanding of the factors that can influence its use. Providing accurate explanations may be a critical factor to help establish trust and increase the likelihood of the operator following an AI agent’s recommendations. However, the impact of explanations on human performance, trust, mental workload (MW) and situation awareness (SA), and confidence are not fully understood. In this paper, we investigate the effects of providing explanations about the agent’s recommendations (of varying levels of accuracy) on human operator’s performance, trust, MW, SA, and confidence compared to an agent that provides no explanations. Thirty individuals were divided into three groups and randomly assigned to an agent accuracy level (high, medium, low). Each participant completed two sessions using the AI agent: one with explanations, one without explanations. In each session, the user’s performance was measured objectively (number of anomalies correctly diagnosed and time to diagnosis), trust, MW, SA, and confidence in their response were measured using surveys. Participants’ performance, trust, SA, and confidence were higher in the sessions when the agent provided explanations for its recommendations. Accuracy had a significant effect on performance, MW and user confidence but not on trust, or SA.

Level of automation effects on performance, situation awareness and workload in a dynamic control task

Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

Unmanned combat air vehicles (UCAV) are finding increasing use in military applications due to their autonomy, processing capabilities and low cost with respect to manned flights. In order to use the UCAVs in the air to air combat, required algorithms must be carefully designed to enhance them with aerial combat competence. There are challanges for the design of this kind of an algorithm such as real-time operability and the case with inadequate number of sensors on the UAVs. In this paper a novel attack algorithm is introduced for UCAVs, with the assumption that in order to launch an effective attack the pursuer aircraft must be able to follow the target aircraft from behind with a predetermined distance. The attack algorithm consists of navigation and reference velocity calculation steps. In navigation part the reference values for bank, heading and flight path angles are computed. In velocity generation part, a PID based method and a fuzzy inference based method are employed to compute the reference velocity of the pursuer and the results obtained with these two approaches are compared. The performance of this algorithm is assessed with simulations performed in three different scenarios with different initial conditions for the pursuer and the target. Simulation results show that the effectiveness of the proposed fuzzy inference based air to air attack algorithm is better in action with respect to PID based algorithm since various effects that cause the change in velocity, can be incorporated into the fuzzy inference mechanism.

BackgroundInadequate situational awareness accounts for two-thirds of preventable complications in anesthesia. An essential tool for situational awareness in the perioperative setting is the patient monitor. However, the conventional monitor has several weaknesses. Avatar-based patient monitoring may address these shortcomings and promote situation awareness, a prerequisite for good decision making.ObjectiveThe spatial distribution of visual attention is a fundamental process for achieving adequate situation awareness and thus a potential quantifiable surrogate for situation awareness. Moreover, measuring visual attention with a head-mounted eye-tracker may provide insights into usage and acceptance of the new avatar-based patient monitoring modality.MethodsThis prospective eye-tracking study compared anesthesia providers' visual attention on conventional and avatar-based patient monitors during simulated critical anesthesia events. We defined visual attention, measured as fixation count and dwell time, as our primary outcome. We correlated visual attention with the potential confounders: performance in managing simulated critical anesthesia events (task performance), work experience, and profession. We used mixed linear models to analyze the results.ResultsFifty-two teams performed 156 simulations. After a manual quality check of the eye-tracking footage, we excluded 57 simulations due to technical problems and quality issues. Participants had a median of 198 (IQR 92.5-317.5) fixations on the patient monitor with a median dwell time of 30.2 (IQR 14.9-51.3) seconds. We found no significant difference in participants' visual attention when using avatar-based patient monitoring or conventional patient monitoring. However, we found that with each percentage point of better task performance, the number of fixations decreased by about 1.39 (coefficient –1.39; 95% CI –2.44 to –0.34; P=.02), and the dwell time diminished by 0.23 seconds (coefficient –0.23; 95% CI: –0.4 to –0.06; P=.01).ConclusionsUsing eye tracking, we found no significant difference in visual attention when anesthesia providers used avatar-based monitoring or conventional patient monitoring in simulated critical anesthesia events. However, we identified visual attention in conjunction with task performance as a surrogate for situational awareness.

The problem of assessing the situation in the control of anthropocentric objects using situational awareness models is considered. In general, the term “situational awareness” is used to describe the degree of adequate assessment of external and internal factors that affect the decision-making of a human operator in the process of controlling a complex object. This problem is not easy due to the complexity and the large number of factors that need to be taken into account to make effective decisions. As a result, situational awareness in the control of anthropocentric objects has a hierarchical structure. The levels of formation of situational awareness in the human operator are given. The place of the situational awareness model in the control system is noted and the relationship of the situation models with human mental models is analyzed. An approach to the formation of situational awareness based on biological principles of constructing mental models and taking into account the functional state of a human operator is proposed.KeywordsHuman operatorAnthropocentric objectsSituational awarenessSituational modelsMental models

A distributed net-centric environment consist of a large variety of data fusion nodes, where each node represents a sensor, software program, machine, human operator, warfighter, or a combat unit. Fusion nodes can be conceptualized as intelligent autonomous agents that communicate, coordinate, and cooperate with each other in order to improve their local situational awareness (SA), and to assess the situation of the operational environment as a whole. In this paper, we describe how we model this net-centric SA problem using a distributed belief propagation paradigm. A local fusion node maintains the joint state of the set of variables modeling a local SA task at hand using Bayesian network (BN) fragments. Local fusion nodes communicate their beliefs and coordinate with each other to update their local estimates of the situation and contribute to the global SA of the environment. We have implemented the propagation paradigm to determine threat out of terrorist dirty bombs with agents searching unstructured intelligence reports for evidence and assessing local situations via BN fragments. The paradigm is a part of our company’s cutting-edge predictive analytics products offering to solve enterprise distributed big data search problem.

Herein we delineate the relationship between poor joint human automation interaction (HAI) system performance and situation awareness (SA) focusing on the effect of system design. Degraded SA is one reason that human users make poor interaction decisions that cause joint system performance to suffer. One key reason for degraded SA is the hierarchical design where the human user is at the apex of the command hierarchy. Within this structure the human user is not able to be fully integrated into the system, which can lead to ‘out of the loop’ performance issues. We propose that SA could be measured in real time by leveraging psychophysiological methods often used in cognitive neuroscience research. We then discuss a potential framework that could not only identify degraded SA in real time, but could mitigate these degradations. Such a framework would be successful because it would genuinely integrate the human user into the system, essentially closing the loop that underlies joint system failures.

Situation awareness is knowing what is going on in the situation. Clinicians working in the emergency medical services (EMS) encounter numerous situations in various conditions, and to be able to provide efficient and patient safe care they need to understand what is going on and possible projections of the current situation. The design of this study encompassed a Goal-Directed Task analysis where situation awareness information requirements were mapped in relation to goals related to various aspects of the EMS mission. A group of 30 EMS subject matter experts were recruited and answered a web-based survey in three rounds related to what they though themselves or a colleague might need to achieve situation awareness related to the specific goals of various situations. The answers were analysed using content analysis and descriptive statistics. Answers reached consensus at a predetermined level of 75%. Those who reached consensus were entered into the final goal-directed task analysis protocol. The findings presented that EMS clinicians must rely on their own, or their colleagues prior experience or knowledge to achieve situation awareness. This suggests that individual expertise plays a crucial role in developing situation awareness. There also seems to be limited support for situation awareness from organizational guidelines. Furthermore, achieving situation awareness also involves collaborative efforts from the individuals involved in the situation. These findings could add to the foundation for further investigation in this area which could contribute to the development of strategies and tools to enhance situation awareness among EMS clinicians, ultimately improving patient care and safety.

Situation awareness (SA) has been widely studied in human operator scenarios as it can have broad effects on operator performance. However, SA often develops and changes rapidly, especially during highly-demanding operational tasks such as piloting aircraft or responding to emergencies. In order to enable just-in-time interventions, systems must have access to the current state of operator SA. Gold-standard objective measures of situation awareness require freeze-probes, a metric that cannot be implemented in operational settings. In order to access this dynamic cognitive state, this work seeks to develop unobtrusive and real-time methods for estimating SA. To do so, this study collected psychophysiological, neurophysiological, behavioral, and performance data while subjects complete the MATB-II, a complex task-battery designed to imitate high-workload scenarios. The task was frozen at regular intervals and subjects completed objective SA assessments and workload questionnaires. Between these freeze-probes, task difficulty and priority were manipulated in order to induce a variety of SA outcomes. Data streams including fNIRS, EEG, ECG, eye-tracking, heart rate, EDA, respiration, and in-task behavioral measures were collected. In correlational analysis, features such as heart rate and skin conductance response were found to be potentially useful data streams for estimating situation awareness. In future work, these and other data streams will be incorporated into models to predict scores on objective freeze-probe assessments in real-time.

Human reliability and the impact of control function allocation in the design of dynamic positioning systems