Técnicas de inteligencia artificial aplicadas a sistemas de detección y clasificación de señales de tráfico.

Esta tesis, presentada como conjunto de artículos de investigación, estudia y analiza soluciones para los sistemas de detección y clasificación de señales de tráfico que suponen un reto en aplicaciones de la actualidad, como son la seguridad y asistencia en carretera a conductores, los coches autónomos, el mantenimiento de señalización vertical, o el análisis de escenas de tráfico. Las señales de tráfico constituyen un activo fundamental dentro de la red decarreteras porque su objetivo es ser fácilmente perceptible por los peatones y conductores para advertirles y guiarlos tanto de día como de noche. El hecho de que las señales estén diseñadas para ser únicas y tener características distinguibles, como formas simples y colores uniformes, implica que su detección y reconocimiento sea un problema limitado. Sin embargo, el desarrollo de un sistema de reconocimiento de señales en tiempo real aún presenta desafíos debido a los tiempos de respuesta, los cuales son cruciales para tomar decisiones en el entorno, y la variabilidad que presentan las imágenes de escenas de tráfico, que pueden incluir imágenes a distintas escalas, puntos de vista complicados, oclusiones, y diferentes condiciones de luz. Cualquier sistema de detección y clasificación de señales de tráfico debe hacer frente a estos retos. En este trabajo, se presenta un sistema de clasificación de señales de tráfico basado en aprendizaje profundo (Deep Learning). Concretamente, los principales componentes de la red neuronal profunda (Deep Neural Network) propuesta, son capas convolucionales y redes de transformaciones espaciales (Spatial Transformer Networks). Dicha red es alimentada con imágenes RGB de señales de tráfico de distintos países como Alemania, Bélgica o España. En el caso de las señales de Alemania, que pertenecen al dataset denominado German Traffic Sign Recognition Benchmark (GTSRB), la arquitectura de red y los parámetros de optimización propuestos obtienen un 99.71% de precisión, mejorando tanto al sistema visual humano como a todos los resultados previos del estado del arte, siendo además más eficiente en términos de requisitos de memoria. En el momento de redactar esta tesis, nuestro método se encuentra en la primera posición de la clasificación a nivel mundial. Por otro lado, respecto a la problemática de la detección de señales de tráfico, se analizan varios sistemas de detección de objetos propuestos en el estado del arte, que son específicamente modificados y adaptados al dominio del problema que nos ocupa para aplicar la transferencia de conocimiento en redes neuronales (transfer learning). También se estudian múltiples parámetros de rendimiento para cada uno de los modelos de detección con el fin de ofrecer al lector cuál sería el mejor detector de señales teniendo en cuenta restricciones del entorno donde se desplegará la solución, como la precisión, el consumo de memoria o la velocidad de ejecución. Nuestro estudio muestra que el modelo Faster R-CNN Inception Resnet V2 obtiene la mejor precisión (95.77% mAP), mientras que R-FCN Resnet 101 alcanza el mejor equilibrio entre tiempo de ejecución (85.45 ms por imagen) y precisión (95.15% mAP)

Improved detection techniques in autonomous vehicles for increased road safety

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2020.A futura adoção em massa de Veículos Autônomos traz um potencial significativo para aumentar a segurança no trânsito para ambos os motoristas e pedestres. Como reportado pelo Departamento de Transportes dos E.U.A., cerca de 94% dos acidentes de trânsito são causados por erro humano. Com essa realidade em mente, a indústria automotiva e pesquisadores acadêmicos ambicionam alcançar direção totalmente automatizada em cenários reais nos próximos anos. Para tal, algorit- mos mais precisos e sofisticados são necessários para que os veículos autônomos possam tomar decisões corretas no tráfego. Nesse trabalho, é proposta uma técnica melhorada de detecção de pedestres, com um aumento de precisão de até 31% em relação aos benchmarks atuais. Em seguida, de forma a acomodar a infraestrutura de trânsito já existente, avançamos a precisão na detecção de placas de trânsito com base em Redes Neurais Convolucionais. Nossa abordagem melhora substancialmente a acurácia em relação ao modelo-base considerado. Finalmente, ap- resentamos uma proposta de fusão de dados precoce, a qual mostramos surpassar abordagens de detecção com um só sensor e fusão de dados tardia em até 20%.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).The future widespread use of Autonomous Vehicles has a significant potential to increase road safety for drivers and pedestrians alike. As reported by the U.S. Department of Transportation, up to 94% of transit accidents are caused by human error. With that reality in mind, the auto- motive industry and academic researches are striving to achieve fully automated driving in real scenarios in the upcoming years. For that, more sophisticated and precise detection algorithms are necessary to enable the autonomous vehicles to take correct decisions in transit. This work proposes an improved technique for pedestrian detection that increases precision up to 31% over current benchmarks. Next, in order to accommodate current traffic infrastructure, we enhance performance of a traffic sign recognition algorithm based on Convolutional Neural Networks. Our approach substantially raises precision of the base model considered. Finally, we present a proposal for early data fusion of camera and LiDAR data, which we show to surpass detection using individual sensors and late fusion by up to 20%

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Mission-Critical Communications from LMR to 5G: a Technology Assessment approach for Smart City scenarios

Radiocommunication networks are one of the main support tools of agencies that carry out actions in Public Protection & Disaster Relief (PPDR), and it is necessary to update these communications technologies from narrowband to broadband and integrated to information technologies to have an effective action before society. Understanding that this problem includes, besides the technical aspects, issues related to the social context to which these systems are inserted, this study aims to construct scenarios, using several sources of information, that helps the managers of the PPDR agencies in the technological decisionmaking process of the Digital Transformation of Mission-Critical Communication considering Smart City scenarios, guided by the methods and approaches of Technological Assessment (TA).As redes de radiocomunicações são uma das principais ferramentas de apoio dos órgãos que realizam ações de Proteção Pública e Socorro em desastres, sendo necessário atualizar essas tecnologias de comunicação de banda estreita para banda larga, e integra- las às tecnologias de informação, para se ter uma atuação efetiva perante a sociedade . Entendendo que esse problema inclui, além dos aspectos técnicos, questões relacionadas ao contexto social ao qual esses sistemas estão inseridos, este estudo tem por objetivo a construção de cenários, utilizando diversas fontes de informação que auxiliem os gestores destas agências na tomada de decisão tecnológica que envolve a transformação digital da Comunicação de Missão Crítica considerando cenários de Cidades Inteligentes, guiado pelos métodos e abordagens de Avaliação Tecnológica (TA)

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

The upcoming 5th Generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated Artificial Intelligence (AI) operations. However, fully-intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the 6th Generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performanceandservicetypes.Theincreasinglystringentperformancerequirementsofemergingnetworks may finally trigger the deployment of some interesting new technologies such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications and cell-free communications – tonameafew.Ourvisionfor6Gis–amassivelyconnectedcomplexnetworkcapableofrapidlyresponding to the users’ service calls through real-time learning of the network state as described by the network-edge (e.g., base-station locations, cache contents, etc.), air interface (e.g., radio spectrum, propagation channel, etc.), and the user-side (e.g., battery-life, locations, etc.). The multi-state, multi-dimensional nature of the network state, requiring real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of Machine Learning (ML), Quantum Computing (QC), and Quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensivereviewoftherelatedstate-of-the-artinthedomainsofML(includingdeeplearning),QCand QML, and identify their potential benefits, issues and use cases for their applications in the B5G networks. Subsequently,weproposeanovelQC-assistedandQML-basedframeworkfor6Gcommunicationnetworks whilearticulatingitschallengesandpotentialenablingtechnologiesatthenetwork-infrastructure,networkedge, air interface and user-end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions