VIDEO GAMES AS TECHNOLOGICAL RUINS OF A RECENT PAST

The aesthetic cultivation of societal recollection is inextricably linked to the approach and preservation of the objects and the mnemonics that accompany them. This perception in modern society includes in its dormitories the historical evolution and retrospect of technology whose origins are pervasive in the realization of social impulses. Nostalgia and memories are no longer moved only through traditional ruins that reflect the aesthetics and sense of social development. In this context, it is important to examine whether video games nowadays can classify the early technological structures into the cradle of modern ruins, the evolutionary course of social memory. This article scrutinizes the multi-dimensional aspects of video games, approaching them as historical and technological achievements and identifying their aesthetic value as objects of the recent past that further stimulate quests lurking in this pixelated romanticism

Identification and characterization of traffic flow patterns for UTM application

The current airspace has limited resource, and the widespread use of Unmanned Aircraft System (UAS) is increasing the density of civilian aircraft that is already crowded with manned aerial vehicles. This increased density in airspace demands to improve the safety, efficiency and capacity of airspace while considering all uncertain parameters that may cause hinderance in aircraft movement like weather and dynamic fluctuations. A systematic analysis of correlations between events and their impacts in air traffic network is a considerable challenge. This paper proposes a methodology that characterizes and identifies the patterns of Unmanned Traffic Management (UTM) airspace based on the analysis of simulated data to improve the performance of UTM network as well as ensuring its safety and capacity. Some sets of metrics are defined to identify the airspace characteristics that include airspace density, capacity and efficiency. The data analysis carried out here, will support risk analysis and improve trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather conditions, loss of safe distances, UAVs’ performance, emergency services and airspace structures that may cause deviations from their standard paths

Traffic flow prediction for UTM application: a deep learning approach

Over the past few years, the research community has focused greatly on predicting air traffic flows, yielding remarkable outcomes. We found that existing literature in the field mainly covers prediction of air traffic flows for conventional aircraft. However, there is limited research about prediction of air traffic flows for Uncrewed Aircraft Traffic Management (UTM). This research study proposes a deep learning-based approach to predict air traffic congestion in the context of UTM over a period of three minutes. The use of the model aims to address congestion considering air traffic uncertainties instead of addressing the conventional issues of trajectory prediction or conflict detection and resolution. Our model also considers the influence of recreational users who fly UAVs at random times, during the execution of the above essential missions. Further, the effects of airspace structure configurations like static No-Fly Zones (NFZ), airfields with variable availability for drone flights, recreational areas, emergency UTM operation and environmental factors such as weather conditions have also been studied. The proposed model shows better performance compared to other approaches such as the Shallow neural networks and regression models

Deep learning architecture for UAV traffic-density prediction

The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average

Assuring safe and efficient operation of UAV using explainable machine learning

The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements

Modeling and characterization of traffic flow patterns and identification of airspace density for UTM application

Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs’ trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas represe ting safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management

An explainable artificial intelligence (xAI) framework for improving trust in automated ATM tools

With the increased use of intelligent Decision Support Tools in Air Traffic Management (ATM) and inclusion of non-traditional entities, regulators and end users need assurance that new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are trustworthy and safe. Although there is a wide amount of research on the technologies themselves, there seem to be a gap between research projects and practical implementation due to different regulatory and practical challenges including the need for transparency and explainability of solutions. In order to help address these challenges, a novel framework to enable trust on AI-based automated solutions is presented based on current guidelines and end user feedback. Finally, recommendations are provided to bridge the gap between research and implementation of AI and ML-based solutions using our framework as a mechanism to aid advances of AI technology within ATM

Hybrid deep neural networks for drone high level intent classification using non-cooperative radar data

The proliferation of drones has brought many benefits in different industrial and government sectors due to their low cost and potential applications. Nevertheless, the security and air space can be compromised due to anomalous performances derived to negligence or intentional malicious activities. Thus, identify the hidden intentions of drones’ mission profiles is paramount to execute adequate countermeasures. In this paper, an hybrid deep neural network architecture is proposed to classify the high level intent of drones’ mission profiles using non-cooperative radar. Radar measurements are created synthetically using open access telemetry data of flight trajectories. The proposed architecture exploits the classification and reconstruction capabilities of deep neural models to classify the drones hidden high-level intent. Several experiments and comparisons are carried out to verify the effectiveness of the proposed approach.Royal Academy of Engineering and Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme

A deep mixture of experts network for drone trajectory intent classification and prediction using non-cooperative radar data

The intent prediction of unmanned aerial vehicles (UAVs) also known as drones is a challenging task due to the different mission profiles and tasks that the drone can perform. To alleviate this issue, this paper proposes a deep mixture of experts network to classify and predict drones trajectories measured from non-cooperative radars. Telemetry data of open-access datasets are converted to simulated radar tracks to generate a pool of heterogeneous trajectories and construct three independent datasets to train, validate, and test the proposed architecture. The network is composed of two main components: i) a deep network that predicts the class associated to the input trajectories and ii) a set of deep experts models that learns the extreme bounds of the trajectories in different future time steps. The proposed approach is tested and compared with different deep models to verify its effectiveness under different flight profiles and time-windows