A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

Airspace complexity is a critical metric in current Air Traffic Management systems for indicating the security degree of airspace operations. Airspace complexity can be affected by many coupling factors in a complicated and nonlinear way, making it extremely difficult to be evaluated. In recent years, machine learning has been proved as a promising approach and achieved significant results in evaluating airspace complexity. However, existing machine learning based approaches require a large number of airspace operational data labeled by experts. Due to the high cost in labeling the operational data and the dynamical nature of the airspace operating environment, such data are often limited and may not be suitable for the changing airspace situation. In light of these, we propose a novel unsupervised learning approach for airspace complexity evaluation based on a deep neural network trained by unlabeled samples. We introduce a new loss function to better address the characteristics pertaining to airspace complexity data, including dimension coupling, category imbalance, and overlapped boundaries. Due to these characteristics, the generalization ability of existing unsupervised models is adversely impacted. The proposed approach is validated through extensive experiments based on the real-world data of six sectors in Southwestern China airspace. Experimental results show that our deep unsupervised model outperforms the state-of-the-art methods in terms of airspace complexity evaluation accuracy

Supporting Validation of UAV Sense-and-Avoid Algorithms with Agent-Based Simulation and Evolutionary Search

A Sense-and-Avoid (SAA) capability is required for the safe integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace. Given their safety-critical nature, SAA algorithms must undergo rigorous verification and validation before deployment. The validation of UAV SAA algorithms requires identifying challenging situations that the algorithms have difficulties in handling. By building on ideas from Search-Based Software Testing, this thesis proposes an evolutionary-search-based approach that automatically identifies such situations to support the validation of SAA algorithms. Specifically, in the proposed approach, the behaviours of UAVs under the control of selected SAA algorithms are examined with agent-based simulations. Evolutionary search is used to guide the simulations to focus on increasingly challenging situations in a large search space defined by (the variations of) parameters that configure the simulations. An open-source tool has been developed to support the proposed approach so that the process can be partially automated. Positive results were achieved in a preliminary evaluation of the proposed approach using a simple two-dimensional SAA algorithm. The proposed approach was then further demonstrated and evaluated using two case studies, applying it to a prototype of an industry-level UAV collision avoidance algorithm (specifically, ACAS XU) and a multi-UAV conflict resolution algorithm (specifically, ORCA-3D). In the case studies, the proposed evolutionary-search-based approach was empirically compared with some plausible rivals (specifically, random-search-based approaches and a deterministic-global-search-based approach). The results show that the proposed approach can identify the required challenging situations more effectively and efficiently than the random-search-based approaches. The results also show that even though the proposed approach is a little less competitive than the deterministic-global-search-based approach in terms of effectiveness in relatively easy cases, it is more effective and efficient in more difficult cases, especially when the objective function becomes highly discontinuous. Thus, the proposed evolutionary-search-based approach has the potential to be used for supporting the validation of UAV SAA algorithms although it is not possible to show that it is the best approach

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

Aircraft Trajectory Planning Considering Ensemble Forecasting of Thunderstorms

Mención Internacional en el título de doctorConvective weather poses a major threat that compromises the safe operation of flights while inducing delay and cost. The aircraft trajectory planning problem under thunderstorm evolution is addressed in this thesis, proposing two novel heuristic approaches that incorporate uncertainties in the evolution of convective cells. In this context, two additional challenges are faced. On the one hand, studies have demonstrated that given the computational power available nowadays, the best way to characterize weather uncertainties is through ensemble forecasting products, hence compatibility with them is crucial. On the other hand, for the algorithms to be used during a flight, they must be fast and deliver results in a few seconds. As a first methodology, three variants of the Scenario-Based Rapidly-Exploring Random Trees (SB-RRTs) are proposed. Each of them builds a tree to explore the free airspace during an iterative and random process. The so-called SB-RRT, the SB-RRT∗ and the Informed SB-RRT∗ find point-to-point safe trajectories by meeting a user-defined safety threshold. Additionally, the last two techniques converge to solutions of minimum flight length. In a second instance, the Augmented Random Search (ARS) algorithm is used to sample trajectories from a directed graph and deform them iteratively in the search for an optimal path. The aim of such deformations is to adapt the initial graph to the unsafe set and its possible changes. In the end, the ARS determines the population of trajectories that, on average, minimizes a combination of flight time, time in storms, and fuel consumption Both methodologies are tested considering a dynamic model of an aircraft flying between two waypoints at a constant flight level. Test scenarios consist of realistic weather forecasts described by an ensemble of equiprobable members. Moreover, the influence of relevant parameters, such as the maximum number of iterations, safety margin (in SB-RRTs) or relative weights between objectives (in ARS) is analyzed. Since both algorithms and their convergence processes are random, sensitivity analyses are conducted to show that after enough iterations the results match. Finally, through parallelization on graphical processing units, the required computational times are reduced substantially to become compatible with near real-time operation. In either case, results show that the suggested approaches are able to avoid dangerous and uncertain stormy regions, minimize objectives such as time of flight, flown distance or fuel consumption and operate in less than 10 seconds.Los fenómenos convectivos representan una gran amenaza que compromete la seguridad de los vuelos, a la vez que incrementa los retrasos y costes. En esta tesis se aborda el problema de la planificación de vuelos bajo la influencia de tormentas, proponiendo dos nuevos métodos heurísticos que incorporan incertidumbre en la evolución de las células convectivas. En este contexto, se intentará dar respuesta a dos desafíos adicionales. Por un lado, hay estudios que demuestran que, con los recursos computacionales disponibles hoy en día, la mejor manera de caracterizar la incertidumbre meteorológica es mediante productos de tipo “ensemble”. Por tanto, la compatibilidad con ellos es crucial. Por otro lado, para poder emplear los algoritmos durante el vuelo, deben de ser rápidos y obtener resultados en pocos segundos. Como primera aproximación, se proponen tres variantes de los “Scenario-Based Rapidly-Exploring Random Trees” (SB-RRTs). Cada uno de ellos crea un árbol que explora el espacio seguro durante un proceso iterativo y aleatorio. Los denominados SB-RRT, SB-RRT∗ e Informed SB-RRT∗ calculan trayectorias entre dos puntos respetando un margen de seguridad impuesto por el usuario. Además, los dos últimos métodos convergen en soluciones de mínima distancia de vuelo. En segundo lugar, el algoritmo “Augmented Random Search” (ARS) se utiliza para muestrear trajectorias de un grafo dirigido y deformarlas iterativamente en busca del camino óptimo. El fin de tales deformaciones es adaptar el grafo inicial a las zonas peligrosas y a los cambios que puedan sufrir. Finalmente, el ARS calcula aquella población de trayectorias que, de media, minimiza una combinación del tiempo de vuelo, el tiempo en zonas tormentosas y el consumo de combustible. Ambas metodologías se testean considerando un modelo de avión volando punto a punto a altitud constante. Los casos de prueba se basan en datos meteorológicos realistas formados por un grupo de predicciones equiprobables. Además, se analiza la influencia de los parámetros más importantes como el máximo número de iteraciones, el margen de seguridad (en SB-RRTs) o los pesos relativos de cada objetivo (en ARS). Como ambos algoritmos y sus procesos de convergencia son aleatorios, se realizan análisis de sensibilidad para mostrar que, tras suficientes iteraciones, los resultados coinciden. Por último, mediante técnicas de paralelización en procesadores gráficos, se reducen enormemente los tiempos de cálculo, siendo compatibles con una operación en tiempo casi-real. En ambos casos los resultados muestran que los algoritmos son capaces de evitar zonas inciertas de tormenta, minimizar objetivos como el tiempo de vuelo, la distancia recorrida o el consumo de combustible, en menos de 10 segundos de ejecución.Programa de Doctorado en Ingeniería Aeroespacial por la Universidad Carlos III de MadridPresidente: Ernesto Staffetti Giammaria.- Secretario: Alfonso Valenzuela Romero.- Vocal: Valentin Polishchu

An Empirical Methodology for Engineering Human Systems Integration

The systems engineering technical processes are not sufficiently supported by methods and tools that quantitatively integrate human considerations into early system design. Because of this, engineers must often rely on qualitative judgments or delay critical decisions until late in the system lifecycle. Studies reveal that this is likely to result in cost, schedule, and performance consequences. This dissertation presents a methodology to improve the application of systems engineering technical processes for design. This methodology is mathematically rigorous, is grounded in relevant theory, and applies extant human subjects data to critical systems development challenges. The methodology is expressed in four methods that support early systems engineering activities: a requirements elicitation method, a function allocation method, an input device design method, and a display layout design method. These form a coherent approach to early system development. Each method is separately discussed and demonstrated using a prototypical system development program. In total, this original and significant work has a broad range of systems engineer applicability to improve the engineering of human systems integration

Impacte da interação entre veículos motorizados e bicicletas na escolha de rota, desempenho de tráfego, emissões e segurança

Mobility in urban areas is highly complex because of the variety of possible facilities and routes, the multitude of origins and destinations, the increase of population and traffic. Increased use of active modes, such as cycling, presents economic and environmental benefits, and contributes to health improvement. However, it can lead to safety concerns such as bicycles sudden or unexpected movements mainly when circulating together with motor vehicles (MVs) or when there is an overtaking situation between MVs and bicycles. The main goal of this doctoral thesis is to quantify and assess the impact of the interaction motor vehicle-bicycle on traffic performance, road safety and emissions to define a multi-objective analysis model of the impacts regarding the use of motor vehicle and/or bicycle. The thesis was focused on three main topics developed based on the evaluation of traffic performance, safety and emissions at urban areas : (i) to perform a multi-objective analysis in an integrated manner of the traffic performance, pollutant emissions and road conflicts between bicycles and MVs at a signalized intersection; (ii) to assess the driving volatility in MV-bicycle interactions at two-lane roundabouts and its impacts on safety, pollutant emissions and traffic performance; and (iii) to analyze the impacts of the overtaking lateral distance between a bicycle and a MV on road safety and energy consumption at two-lane urban roads. Second-by-second bicycle and vehicle dynamic data were collected using GPS travel recorders. The methodology developed in this thesis was applied based on real world case studies at different urban road types in the city of Aveiro, Portugal. The present work uses a microscopic simulation platform of traffic (VISSIM), road safety (Surrogate Safety Assessment Methodology – SSAM) and emissions (Vehicle Specific Power – VSP) to analyze traffic operations, road safety concerns and to estimate carbon dioxide (CO2), nitrogen oxide (NOX), carbon monoxide (CO), and hydrocarbons (HC) pollutant emissions. Furthermore, the Fast Non-Dominated Sorting Genetic Algorithm (NSGA-II) was used in order to address the multi-objective analysis of traffic performance, road conflicts between MVs and bicycles, and emissions. Bicycle Specific Power (BSP) and VSP concepts were used in order to analyze the impacts on cyclist and vehicle energy consumption as well. The findings showed that roundabouts present, in general, better traffic performance (number of stops and travel time reduced in 78% and 14%, respectively) and less emissions (CO2, NOX, and HC decreased 9%, 7%, and 12%, respectively) than other intersections, even with high demand of cyclists (270 bicycles per hour). Regarding safety, roundabout layout lead to more severe conflicts and potential crashes while the number of total conflicts can be reduced significantly (-49%). It was also found that the impact of MVs and bicycles speeds, as well as roundabout design, were more important factors than bicycle volumes at roundabouts. Considering the MV-bicycle interaction at two-lane roundabout, the results of emissions dictated good relationships (R2 > 70%) between acceleration and VSP modes distributions. Finally, the findings showed 50% of overtaking lateral distance (between bicycle and MV) lower than 0.5m in both morning and afternoon peak hours. Moreover, it was found that there was a good fit between overtaking lateral distance and traffic volumes in morning (R2 = 72%) and afternoon (R2 = 67%) peak hours. The findings of this research can be useful for policy makers of the mobility and road safety fields, municipalities, road designers, and traffic engineers.A complexidade inerente à mobilidade em áreas urbanas está associada ao excesso de tráfego e à multiplicidade de origem-destinos, rotas e motivos de viagem. O incremento do uso dos modos suaves, nomeadamente da bicicleta, apresenta benefícios económicos e ambientais, contribuindo para a melhoria da saúde. No entanto, a presença de bicicletas acarreta preocupações ao nível da segurança dos ciclistas. As questões de segurança podem estar relacionadas com movimentos súbitos ou inesperados dos ciclistas, principalmente quando circulam em conjunto com veículos motorizados (VMs), ou quando há uma situação de ultrapassagem entre VMs e bicicletas. O principal objetivo da Tese de Doutoramento consistiu em quantificar e avaliar o impacto da interação entre veículos motorizados e bicicletas ao nível do desempenho de tráfego, segurança rodoviária e emissões para definir um modelo de análise multiobjetivo. A tese foi focada em três tópicos principais, desenvolvidos com base na avaliação do desempenho do tráfego, segurança e emissões em áreas urbanas: (i) análise multiobjetivo de forma integrada do desempenho do tráfego, emissões poluentes e conflitos rodoviários entre bicicletas e VMs em intersecções sinalizadas; (ii) avaliação da volatilidade de condução em interações VM-bicicleta em rotundas de duas vias e seus impactos na segurança, emissões de poluentes e desempenho de tráfego; e (iii) análise dos impactos ao nível de segurança rodoviária e consumo de energia em vias urbanas, com a avaliaçao da distância lateral de ultrapassagem entre uma bicicleta e um VM. Os dados da dinâmica do velocípede e do VM foram recolhidos e gravados segundo a segundo com um GPS. A metodologia desenvolvida nesta tese foi aplicada tendo por base os estudos de caso associados a diferentes tipos de vias urbanas na cidade de Aveiro, Portugal. O presente trabalho utiliza uma plataforma de simulação microscópica de tráfego (VISSIM), segurança rodoviária (SSAM) e emissões (Potência Específica do Veículo - VSP) para analisar as operações relacionadas com tráfego, questões com segurança rodoviária e estimar o dióxido de carbono (CO2), emissões de poluentes como o óxido de azoto (NOX), monóxido de carbono (CO) e hidrocarbonetos (HC). Além disso, para a análise multiobjetivo do desempenho do tráfego, conflitos rodoviários entre VMs e bicicletas, e emissões, o algoritmo genético NSGA-II (Nondominated sorted genetic algorithm II) foi utilizado. As metodologias de Potência Específica de Bicicleta (BSP) e VSP foram usados para analisar os impactos no consumo de energia do ciclista e do veículo, respetivamente. Os resultados mostraram que, em geral, as rotundas apresentam melhor desempenho de tráfego (número de paragens e tempo de viagem reduzidos em 78% e 14%, respetivamente) e menores emissões (CO2, NOX e HC diminuíram 9%, 7% e 12%, respetivamente) quando comparadas a outras interseções, mesmo com elevados níveis de ciclistas (270 bicicletas por hora). Em relação à segurança, o design da rotunda tende a favorecer a ocorrência de conflitos mais graves e potenciais acidentes, apesar do número total de conflitos poder diminuir significativamente (menos 49%). Descobriu-se também que o impacto das velocidades de circulação dos VMs e das bicicletas, bem como o design da rotunda constituem fatores mais importantes do que o volume de ciclistas nas rotundas. Considerando a interação VM-bicicleta numa rotunda de duas vias, os resultados das emissões sugerem boas relações (R2> 70%) entre as distribuições dos modos de aceleração e VSP. Por fim, os resultados mostraram que em 50% das ultrapassagens a distância lateral entre o velocípede e o VM foi menor que 0,5m, tanto na hora de ponta da manhã como da tarde. Além disso, verificou-se um bom ajuste entre a distância lateral de ultrapassagem e os volumes de tráfego nas horas de ponta da manhã (R2 = 72%) e da tarde (R2 = 67%). A metodologia e resultados desta investigação poderão ser utilizados por decisores políticos na área da mobilidade e da segurança rodoviária, câmaras, gestores e engenheiros de tráfego.Programa Doutoral em Engenharia Mecânic

A study on an integrated observation and collision avoiding support system for merchant ships

東京海洋大学博士学位論文 平成23年度(2011) 応用環境システム学 課程博士 甲第253号指導教員: 大津皓平全文公表年月日: 2016-12-13東京海洋大学201

The applications of satellites to communications, navigation and surveillance for aircraft operating over the contiguous United States. Volume 1 - Technical report

Satellite applications to aircraft communications, navigation, and surveillance over US including synthesized satellite network and aircraft equipment for air traffic contro

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence