“Make no little plans”: Impactful research to solve the next generation of transportation problems

The transportation science research community has contributed to numerous practical and intellectual innovations and improvements over the last decades. Technological advancements have broadened and amplified the potential impacts of our field. At the same time, the world and its communities are facing greater and more serious challenges than ever before. In this paper, we call upon the transportation science research community to work on a research agenda that addresses some of the most important of these challenges. This agenda is guided by the sustainable development goals outlined by the United Nations and organized into three areas: (1) well-being, (2) infrastructure, and, (3) natural environment. For each area, we identify current and future challenges as well as research directions to address those challenges

Journal of Telecommunications and Information Technology

kwartalni

Towards 6G Through SDN and NFV-Based Solutions for Terrestrial and Non-Terrestrial Networks

As societal needs continue to evolve, there has been a marked rise in a wide variety of emerging use cases that cannot be served adequately by existing networks. For example, increasing industrial automation has not only resulted in a massive rise in the number of connected devices, but has also brought forth the need for remote monitoring and reconnaissance at scale, often in remote locations characterized by a lack of connectivity options. Going beyond 5G, which has largely focused on enhancing the quality-of-experience for end devices, the next generation of wireless communications is expected to be centered around the idea of "wireless ubiquity". The concept of wireless ubiquity mandates that the quality of connectivity is not only determined by classical metrics such as throughput, reliability, and latency, but also by the level of coverage offered by the network. In other words, the upcoming sixth generation of wireless communications should be characterized by networks that exhibit high throughput and reliability with low latency, while also providing robust connectivity to a multitude of devices spread across the surface of the Earth, without any geographical constraints. The objective of this PhD thesis is to design novel architectural solutions for the upcoming sixth generation of cellular and space communications systems with a view to enabling wireless ubiquity with software-defined networking and network function virtualization at its core. Towards this goal, this thesis introduces a novel end-to-end system architecture for cellular communications characterized by innovations such as the AirHYPE wireless hypervisor. Furthermore, within the cellular systems domain, solutions for radio access network design with software-defined mobility management, and containerized core network design optimization have also been presented. On the other hand, within the space systems domain, this thesis introduces the concept of the Internet of Space Things (IoST). IoST is a novel cyber-physical system centered on nanosatellites and is capable of delivering ubiquitous connectivity for a wide variety of use cases, ranging from monitoring and reconnaissance to in-space backhauling. In this direction, contributions relating to constellation design, routing, and automatic network slicing form a key aspect of this thesis.Ph.D

Minimum time search of moving targets in uncertain environments

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 19-07-2013Esta tesis aborda el desarrollo de un sistema autónomo para buscar un objetivo móvil en el menor tiempo posible sobre un entorno con incertidumbre, es decir, para resolver el problema de búsqueda de tiempo mínimo, que se presenta como un problema especial dentro de la teoría de búsqueda óptima. Se propone una solución Bayesiana para encontrar el objetivo utilizando varios agentes móviles con dinámica restringida provistos de sensores que proporcionan información del entorno. La búsqueda de tiempo mínimo involucra dos procesos: la estimación de la ubicación del objetivo a partir de la información recogida por los agentes que cooperan en la búsqueda, y el diseño de la planificación de las rutas que deben seguir los agentes para encontrar el objetivo. La estimación de la ubicación del objetivo se aborda utilizando técnicas Bayesianas, más específicamente, el filtro recursivo Bayesiano. Además, se propone un filtro de información, basado en el filtro de Kalman extendido, que afronta el problema de los retrasos en la comunicación (problema de medidas desordenadas). La planificación de las trayectorias de los agentes se plantea como un problema de decisión secuencial donde, a partir de la estimación de la ubicación del objetivo, se calculan las mejores acciones que los agentes tienen que realizar. Para ello se proponen tres estrategias Bayesianas: minimización del tiempo local de detección esperado, maximización de la probabilidad de detección descontada por una función dependiente del tiempo, y optimización de una función probabilística que integra una heurística que aproxima la observación esperada. Para implementar las estrategias se proponen tres soluciones. La primera, basada en la programación con restricciones, ofrece soluciones exactas para el caso discreto cuando el objeto es estático y el número de variables de decisión pequeño. La segunda es un algoritmo aproximado construido a partir del método de optimización de entropía cruzada que aborda el caso discreto para objetos dinámicos. La tercera es un algoritmo descentralizado basado en el método del gradiente que calcula decisiones en un horizonte limitado, teniendo en cuenta el futuro, en el caso continuo. Los problemas de búsqueda de tiempo mínimo se encuentran en el planteamiento de muchas aplicaciones reales, como son las operaciones de emergencia de búsqueda y rescate (p.e. rescate de náufragos en accidentes marítimos) o el control de la difusión de sustancias contaminantes (p.e. monitorización de derrames de petróleo). Esta tesis muestra cómo reducir el tiempo de búsqueda de un objeto móvil de forma eficiente, determinando qué estrategias de búsqueda tienen en cuenta el tiempo y bajo qué condiciones son válidas, y proporcionando algoritmos polinómicos que calculen las acciones que los agentes tienen que realizar para encontrar el objeto.This thesis is concerned with the development of an autonomous system to search a dynamic target in the minimum possible time in uncertain environments, that is, to solve the minimum time search problem, which is presented as an especial problem within the optimal search theory. This work proposes a Bayesian approach to nd the target using several moving agents with constrained dynamics and equipped with sensors that provide information about the environment. The minimum time search involves two process: the target location estimation using the information collected by the agents, and the planning of the searching routes that the agents must follow to nd the target. The target location estimation is tackled using Bayesian techniques, more precisely, the recursive Bayesian lter. Moreover, an improved information lter, based on the extended Kalman lter, that deals with the team communication delays (i.e. out of sequence problem) is presented. The agents trajectory planning is faced as a sequential decision making problem where, given the a priori target location estimation, the best actions that the agents have to perform are computed. For that purpose, three Bayesian strategies are proposed: minimizing the local expected time of detection, maximizing the discounted time probability of detection, and optimizing a probabilistic function that integrates an heuristic that approximates the expected observation. To implement the strategies, three solutions are proposed. The rst one, based on constraint programming, provides exact solutions in the discrete case when the target is static and the number of decision variables is small. The second one is an approximated algorithm stood on the cross entropy optimization method that tackles the discrete case for dynamic targets. The third solution is a gradient-based decentralized algorithm that achieves non-myopic solutions for the continuous case. The minimum time search problems are found inside the core of many real applications, such as search and rescue emergency operations (e.g. shipwreck accidents) or pollution substances di usion control (e.g. oil spill monitoring). This thesis reveals how to reduce the searching time of a moving target e ciently, determining which searching strategies take into account the time and under which conditions are valid, and providing approximated polynomial algorithms to compute the actions that the agents must perform to find the target.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Ein Beitrag zur taktischen Verhaltensplanung für Fahrstreifenwechsel bei automatisierten Fahrzeugen

Automated driving within one lane is a fascinating experience. Yet, it is even more interesting to go a step ahead: Making automated lane changes without human driver interaction. This thesis presents a concept and implementation demonstrated in "Jack", the Audi A7 piloted driving concept vehicle. Given that automated driving is in the media every other day already, why is it still such a big issue to do tactical behavior planning for automated vehicles? It is one of the core areas where it is surprisingly obvious why humans are currently so much smarter than machines: Tactical driving behavior planning is a social task that requires cooperation, intention recognition, and complex situation assessment. Without complex cognitive capabilities in today's automated vehicles, it is core of this thesis to find simple algorithms that pretend intelligence in behavior planning. In fact, such behavior planning in automated driving is a constant trade-off between utility and risk: The vehicle has to balance value dimensions such as safety, legality, mobility, and additional aspects like creating user and third party satisfaction. This thesis provides a framework to boil down such abstract dimensions into a working implementation. Several of the foundations for this thesis were developed as part of the Stadtpilot project at TU Braunschweig. While there has been plenty of research on concepts being tested in perfect, simulated worlds only, the approaches in this thesis have been implemented and evaluated in real world traffic with uncertain and imperfect sensor data. The implementation has been tested, tweaked, and used in "Jack" for more than 50,000 km of automated driving in everyday traffic.Automatisiertes Fahren innerhalb eines Fahrstreifens ist eine faszinierende Erfahrung. Noch spannender ist es jedoch noch einen Schritt weiter zu gehen: Auch Fahrstreifenwechsel automatisiert auszuführen, ohne Interaktion mit einem Menschen als Fahrer. In dieser Dissertation wird hierfür ein Konzept und dessen Umsetzung in „Jack“ präsentiert, dem Audi A7 piloted driving concept Fahrzeug. Automatisiertes Fahren ist aktuell in den Medien in aller Munde. Warum ist es dennoch eine große Herausforderung taktische Verhaltensplanung für automatisierte Fahrzeuge wirklich umzusetzen? Es ist einer der Kernbereiche, in denen offensichtlich wird, warum Menschen aktuell Maschinen im Straßenverkehr noch weitaus überlegen sind: Taktische Verhaltensplanung ist eine soziale Aufgabe, welche Kooperation, das Erkennen von Absichten und der Bewertung komplexer Situationen bedarf. Mangels wirklicher kognitiver Fähigkeiten in den heutigen automatisierten Fahrzeugen ist es Kern dieser Dissertation Algorithmen zu finden, welche zumindest den Eindruck intelligenter Verhaltensplanung erzeugen. Eine solche Verhaltensplanung ist ein permanentes Abwägen von Nutzen und Risiken. Das Fahrzeug muss permanent Entscheidungen im Spannungsfeld zwischen Sicherheit, Legalität, Mobilität und weiten Aspekten wie Nutzerzufriedenheit und Zufriedenheit Dritter treffen. In dieser Dissertation wird ein Konzept entwickelt, um solche abstrakten Entscheidungsdimensionen in ein implementierbares Konzept herunterzubrechen. Viele Grundlagen dafür wurden im Rahmen des Stadtpilot Projekts der TU Braunschweig erarbeitet. In vorausgehenden Arbeiten wurden bereits viele Ansätze entwickelt und auf Basis von perfekten, simulierten Daten evaluiert. Der in dieser Arbeit präsentierte Ansatz ist in der Lage mit unsicherheits- und fehlerbehafteten Messdaten umzugehen. Der Ansatz aus dieser Dissertation wurde in dem automatisiert fahrenden Fahrzeug „Jack“ implementiert und bereits über 50.000 km im normalen Straßenverkehr genutzt und getestet

Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Estimating the efficacy of mass rescue operations in ocean areas with vehicle routing models and heuristics

Tese de doutoramento, Estatística e Investigação Operacional (Optimização), Universidade de Lisboa, Faculdade de Ciências, 2018Mass rescue operations (MRO) in maritime areas, particularly in ocean areas, are a major concern for the authorities responsible for conducting search and rescue (SAR) activities. A mass rescue operation can be defined as a search and rescue activity characterized by the need for immediate assistance to a large number of persons in distress, such that the capabilities normally available to search and rescue are inadequate. In this dissertation we deal with a mass rescue operation within ocean areas and we consider the problem of rescuing a set of survivors following a maritime incident (cruise ship, oil platform, ditched airplane) that are drifting in time. The recovery of survivors is performed by nearby ships and helicopters. We also consider the possibility of ships capable of refuelling helicopters while hovering which can extend the range to which survivors can be rescued. A linear binary integer formulation is presented along with an application that allows users to build instances of the problem. The formulation considers a discretization of time within a certain time step in order to assess the possibility of travelling along different locations. The problem considered in this work can be perceived as an extension of the generalized vehicle routing problem (GVRP) with a profit stance since we may not be able to recover all of the survivors. We also present a look ahead approach, based on the pilot method, to the problem along with some optimal results using state of the art Mixed-integer linear programming solvers. Finally, the efficacy of the solution from the GVRP is estimated for a set of scenarios that combine incident severity, location, traffic density for nearby ships and SAR assets availability and location. Using traffic density maps and the estimated MRO efficacy, one can produce a combined vulnerability map to ascertain the quality of response to each scenario.Marinha Portuguesa, Plano de Atividades de Formação Nacional (PAFN