Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however, due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multiagent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy

Simulation-Based Evolutionary Optimization of Air Traffic Management

In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involve-ment of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS)and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective

Spatiotemporal graph indicators for air traffic complexity analysis

There has been extensive research in formalising air traffic complexity, but existing works focus mainly on a metric to tie down the peak air traffic controllers workload rather than a dynamic approach to complexity that could guide both strategical, pre-tactical and tactical actions for a smooth flow of aircraft. In this paper, aircraft interdependencies are formalized using graph theory and four complexity indicators are described, which combine spatiotemporal topological information with the severity of the interdependencies. These indicators can be used to predict the dynamic evolution of complexity, by not giving one single score, but measuring complexity in a time window. Results show that these indicators can capture complex spatiotemporal areas in a sector and give a detailed and nuanced view of sector complexity

Sistema de transports intel·ligents : diferències de difusió a Europa i els EUA

La mobilitat compartida, la mobilitat com a servei i el vehicle autònom s'han proposat com dues solucions adients que contribuiran al gaudi de ciutats més eficients i amb una millor qualitat de vida per als seus habitants. Investigadors de la UAB estudien en el projecte europeu Newbits, han comparat Europa i els Estats Units (EUA) per conèixer els factors crítics en la seva difusió entre la població. Reticències a l'ús de tecnologies de seguiment o el suport d'autoritats locals en mobilitat compartida i la ciberseguretat i l'ètica en el vehicle autònom en són alguns dels que han detectat.La movilidad compartida, la movilidad como servicio y el vehículo autónomo se han propuesto como dos soluciones adecuadas que contribuirán al disfrute de ciudades más eficientes y con una mejor calidad de vida para sus habitantes. Investigadores de la UAB estudian en el proyecto europeo Newbits, han comparado Europa y los Estados Unidos (EE.UU.) para conocer los factores críticos en su difusión entre la población. Algunos de los factores que han detectado son reticencias al uso de tecnologías de seguimiento, el apoyo de autoridades locales en movilidad compartida y la ciberseguridad y la ética en el vehículo autónomo.Sharing mobility, mobility as a service and autonomous vehicles have been proposed as two suitable solutions that will contribute to the enjoyment of more efficient cities and with a better living quality for its inhabitants. Researchers of the UAB participating in the European project Newbits have compared Europe and the United States (US) to discover the critical factors in its diffusion among the population. Reluctance to the use of tracking technologies and the support of local authorities in sharing mobility and cybersecurity, as well as ethics in the autonomous vehicle are some factors that have been detected

Sistema de transports intel·ligents : diferències de difusió a Europa i els EUA

La mobilitat compartida, la mobilitat com a servei i el vehicle autònom s'han proposat com dues solucions adients que contribuiran al gaudi de ciutats més eficients i amb una millor qualitat de vida per als seus habitants. Investigadors de la UAB estudien en el projecte europeu Newbits, han comparat Europa i els Estats Units (EUA) per conèixer els factors crítics en la seva difusió entre la població. Reticències a l'ús de tecnologies de seguiment o el suport d'autoritats locals en mobilitat compartida i la ciberseguretat i l'ètica en el vehicle autònom en són alguns dels que han detectat.La movilidad compartida, la movilidad como servicio y el vehículo autónomo se han propuesto como dos soluciones adecuadas que contribuirán al disfrute de ciudades más eficientes y con una mejor calidad de vida para sus habitantes. Investigadores de la UAB estudian en el proyecto europeo Newbits, han comparado Europa y los Estados Unidos (EE.UU.) para conocer los factores críticos en su difusión entre la población. Algunos de los factores que han detectado son reticencias al uso de tecnologías de seguimiento, el apoyo de autoridades locales en movilidad compartida y la ciberseguridad y la ética en el vehículo autónomo.Sharing mobility, mobility as a service and autonomous vehicles have been proposed as two suitable solutions that will contribute to the enjoyment of more efficient cities and with a better living quality for its inhabitants. Researchers of the UAB participating in the European project Newbits have compared Europe and the United States (US) to discover the critical factors in its diffusion among the population. Reluctance to the use of tracking technologies and the support of local authorities in sharing mobility and cybersecurity, as well as ethics in the autonomous vehicle are some factors that have been detected

Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots' workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model's training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment