Examining the myths of connected and autonomous vehicles: analysing the pathway to a driverless mobility paradigm

Connected and autonomous vehicles (CAVs) could become the most powerful mobility intervention in the history of human race; possibly greater than the conception of the wheel itself or the shift from horse-carriages to automobiles. Despite CAVs' likely traffic safety, economic, environmental, social inclusion and network performance benefits their full-scale implementation may not be as predictable, uncomplicated, acceptable and risk-free as it is often communicated by a large share of automotive industries, policy-makers and transport experts. Framing an 'unproven', 'disruptive' and 'life-changing' intervention, primarily based on its competitive advantages over today's conventional automobile technologies, may create misconceptions, overreaching expectations and room for errors that societies need to be cautious about. This article 'tests' eleven myths referring to an overly optimistic CAVs' development and adoption timeline. This approach highlights unresolved issues that need to be addressed before an inescapable CAV-based mobility paradigm transition takes place and provides relevant policy recommendations on how to achieve that

Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT

In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV). The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets. This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols. The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety

Achieving reliable and enhanced communication in vehicular ad hoc networks (VANETs)

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirement for the degree of Doctor of PhilosophyWith the envisioned age of Internet of Things (IoTs), different aspects of Intelligent Transportation System (ITS) will be linked so as to advance road transportation safety, ease congestion of road traffic, lessen air pollution, improve passenger transportation comfort and significantly reduce road accidents. In vehicular networks, regular exchange of current position, direction, speed, etc., enable mobile vehicle to foresee an imminent vehicle accident and notify the driver early enough in order to take appropriate action(s) or the vehicle on its own may take adequate preventive measures to avert the looming accident. Actualizing this concept requires use of shared media access protocol that is capable of guaranteeing reliable and timely broadcast of safety messages. This dissertation investigates the use of Network Coding (NC) techniques to enrich the content of each transmission and ensure improved high reliability of the broadcasted safety messages with less number of retransmissions. A Code Aided Retransmission-based Error Recovery (CARER) protocol is proposed. In order to avoid broadcast storm problem, a rebroadcasting vehicle selection metric η, is developed, which is used to select a vehicle that will rebroadcast the received encoded message. Although the proposed CARER protocol demonstrates an impressive performance, the level of incurred overhead is fairly high due to the use of complex rebroadcasting vehicle selection metric. To resolve this issue, a Random Network Coding (RNC) and vehicle clustering based vehicular communication scheme with low algorithmic complexity, named Reliable and Enhanced Cooperative Cross-layer MAC (RECMAC) scheme, is proposed. The use of this clustering technique enables RECMAC to subdivide the vehicular network into small manageable, coordinated clusters which further improve transmission reliability and minimise negative impact of network overhead. Similarly, a Cluster Head (CH) selection metric ℱ(\u1d457) is designed, which is used to determine and select the most suitably qualified candidate to become the CH of a particular cluster. Finally, in order to investigate the impact of available radio spectral resource, an in-depth study of the required amount of spectrum sufficient to support high transmission reliability and minimum latency requirements of critical road safety messages in vehicular networks was carried out. The performance of the proposed schemes was clearly shown with detailed theoretical analysis and was further validated with simulation experiments

Sistema de suporte à decisão para transportes públicos

Mestrado em Engenharia de Computadores e TelemáticaNowadays, the technology to turn cities smart already exists. Smart Cities, as they are called, are capable to sense, analyze and react: sense through the set of sensors displaced along the city, as they are sensors either xed (for environmental monitoring) or moving (for instance, citizens with their smartphones). A notable case is Porto, which incorporates a mesh network with more than 600 vehicles (buses, taxis and garbage trucks), communicating in-between and enabling the passengers of the buses of the city major bus carrier to access freely to the Internet while commuting. A vehicular network like this has huge positive impact in the city mobility, which is one of the biggest concerns of the governmental institutions. Therefore, it is crucial to understand what can be done to improve mobility. By analyzing the data generated by the movement of the buses, it is possible to deliver a new set of tools that might be useful for the everyday life of the bus passengers and bus eet managers. From the passengers perspective, the utility can be brought by the introduction of smart schedules, which consists on delivering estimated time of arrival that is adapting itself to the city dynamics, through the evolution of the time, and that can be accessed directly from their smartphones. From the perspective of the bus eet managers, it is possible to deliver insights about the usual behaviour of their bus lines, giving openness for them to react to the new or abnormal city public transportation dynamics. This dissertation presents an approach for analyzing the data descendent from the vehicular network and how to use it to answer the previously addressed problems. Regarding the missing link between the GPS trace from the bus and the bus line that they are doing, a map-matching algorithm is implemented. That turns possible the computation of estimations and predictions of the bus' passing times. In what concerns prediction, three machine learning ensemble algorithms have been tested. Finally, proof-ofconcept applications are implemented to demonstrate the real-life applicability, by helping the bus passengers and bus eet managers to react to the di erent events of their quotidian. The results show that the map-matching algorithm presents a good quality. Also, they demonstrate that the best machine learning algorithm, considering the prediction error, is Bagging using Support Vector Regressor as the base estimator. Finally, the pro les obtained in the performance dashboard enable distinction between optimal and non-optimal bus lines.Hoje em dia existe tecnologia para tornar as cidades inteligentes. As cidades inteligentes s~ao capazes de sentir, analisar e reagir: sentir através dos variados sensores espalhados em torno da cidade, sensores estes que podem ser fixos (sensores para a monitorização do estado ambiental) ou moveis (por exemplo, os cidadãos, graças aos seus smartphones). Um caso notável e o da cidade do Porto, que incorpora uma rede em malha com mais de 600 veículos (autocarros, táxis e camiões do lixo) que comunicam entre si, habilitando os passageiros dos autocarros da maior operadora da cidade a navegar na internet gratuitamente, enquanto viajam. O maior impacto de uma rede como esta e a mobilidade; e uma das preocupações das instituições governamentais locais e como elas podem melhorar a mobilidade. E por isso crucial analisar o que pode ser feito para melhorar a mobilidade de uma cidade. Utilizando os dados gerados pelo movimento dos autocarros e possível fornecer um conjunto de novas utilidades praticas que podem ser úteis ao quotidiano dos cidadãos e dos gestores de frota. Na perspectiva dos passageiros pode ser introduzido o conceito de smart schedule que consiste em fornecer o tempo estimado de chegada de um autocarro que se vai adaptando ao longo do tempo, de acordo com a dinâmica da cidade, que pode ser acedido directamente a partir do seu smartphone. Na perspectiva dos gestores de frota e possível fornecer introespecções sobre o comportamento habitual das linhas de autocarros, dando abertura a que estes sejam capazes de melhor reagir a novas ou anormais dinâmicas dos transportes públicos da cidade. Esta dissertação apresenta uma abordagem para analisar os dados provenientes da rede veicular e de como usa-los para tornar as ideias previamente esclarecidas, possíveis. Devido a inexistência da identificação do trafico GPS a uma linha de autocarro, um algoritmo de map-matching foi implementado. Isso torna a computação de estimações e predições sobre o tempo de passagem dos autocarros possível. No que toca a predição, foram testados três algoritmos diferentes de aprendizagem automática em conjunto para a construção de modelos preditivos. Porem, foram implementadas aplicações como prova de conceito que demonstram a aplicabilidade no mundo real, ajudando os passageiros dos autocarros e os gestores de frota a reagir aos diferentes eventos do seu quotidiano. Os resultados demonstram que o algoritmo de map-matching apresenta uma boa qualidade. Também demonstram que o melhor algoritmo de aprendizagem automática, considerando o erro de predição, e o Bagging utilizando como estimador base Support Vector Regressor. Porém, os pers obtidos pelo painel de controlo permitem distinguir linhas de autocarro com um funcionamento óptimo daquelas em que o funcionamento e insatisfação

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

13th International Conference on Modeling, Optimization and Simulation - MOSIM 2020

Comité d’organisation: Université Internationale d’Agadir – Agadir (Maroc) Laboratoire Conception Fabrication Commande – Metz (France)Session RS-1 “Simulation et Optimisation” / “Simulation and Optimization” Session RS-2 “Planification des Besoins Matières Pilotée par la Demande” / ”Demand-Driven Material Requirements Planning” Session RS-3 “Ingénierie de Systèmes Basées sur les Modèles” / “Model-Based System Engineering” Session RS-4 “Recherche Opérationnelle en Gestion de Production” / "Operations Research in Production Management" Session RS-5 "Planification des Matières et des Ressources / Planification de la Production” / “Material and Resource Planning / Production Planning" Session RS-6 “Maintenance Industrielle” / “Industrial Maintenance” Session RS-7 "Etudes de Cas Industriels” / “Industrial Case Studies" Session RS-8 "Données de Masse / Analyse de Données” / “Big Data / Data Analytics" Session RS-9 "Gestion des Systèmes de Transport” / “Transportation System Management" Session RS-10 "Economie Circulaire / Développement Durable" / "Circular Economie / Sustainable Development" Session RS-11 "Conception et Gestion des Chaînes Logistiques” / “Supply Chain Design and Management" Session SP-1 “Intelligence Artificielle & Analyse de Données pour la Production 4.0” / “Artificial Intelligence & Data Analytics in Manufacturing 4.0” Session SP-2 “Gestion des Risques en Logistique” / “Risk Management in Logistics” Session SP-3 “Gestion des Risques et Evaluation de Performance” / “Risk Management and Performance Assessment” Session SP-4 "Indicateurs Clés de Performance 4.0 et Dynamique de Prise de Décision” / ”4.0 Key Performance Indicators and Decision-Making Dynamics" Session SP-5 "Logistique Maritime” / “Marine Logistics" Session SP-6 “Territoire et Logistique : Un Système Complexe” / “Territory and Logistics: A Complex System” Session SP-7 "Nouvelles Avancées et Applications de la Logique Floue en Production Durable et en Logistique” / “Recent Advances and Fuzzy-Logic Applications in Sustainable Manufacturing and Logistics" Session SP-8 “Gestion des Soins de Santé” / ”Health Care Management” Session SP-9 “Ingénierie Organisationnelle et Gestion de la Continuité de Service des Systèmes de Santé dans l’Ere de la Transformation Numérique de la Société” / “Organizational Engineering and Management of Business Continuity of Healthcare Systems in the Era of Numerical Society Transformation” Session SP-10 “Planification et Commande de la Production pour l’Industrie 4.0” / “Production Planning and Control for Industry 4.0” Session SP-11 “Optimisation des Systèmes de Production dans le Contexte 4.0 Utilisant l’Amélioration Continue” / “Production System Optimization in 4.0 Context Using Continuous Improvement” Session SP-12 “Défis pour la Conception des Systèmes de Production Cyber-Physiques” / “Challenges for the Design of Cyber Physical Production Systems” Session SP-13 “Production Avisée et Développement Durable” / “Smart Manufacturing and Sustainable Development” Session SP-14 “L’Humain dans l’Usine du Futur” / “Human in the Factory of the Future” Session SP-15 “Ordonnancement et Prévision de Chaînes Logistiques Résilientes” / “Scheduling and Forecasting for Resilient Supply Chains

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions