Integrated Multimodal Transportation Dashboard

Na área dos sistemas de transportes atualmente existem vários sistemas inteligentes que permitem a monitorização, controlo e outras funções relevantes para um dado tipo de transportes. Entretanto, o tratamento individualizado dos diferentes modos, não favorece a geração de políticas e mecanismos integrados de gestão de transporte multimodal; são pouquíssimas as soluções que juntam diferentes tipos de transportes numa só aplicação. Surgiu, portanto, a necessidade dum painel de monitorização multimodal, que permitirá unir vários tipos de sistemas de transportes e fornecerá a visão geral para a observação se todos os sistemas estão funcionais e operantes a um nível de serviço aceitável. Uma vez que tais sistemas fornecem serviços e dados de alcance diferente e com os níveis de qualidade e detalhes variáveis, a detecção de funcionamento abnormal dum sistema é um desafio que requer a identificação, aplicação, adaptação ou criação de métricas de funcionamento normal para cada sistema de transportes, tendo como base os dados fornecidos por protocolos utilizados por ITSs integrados na solução. Este problema é abordado por projeto "Integrated Multimodal Transportation Dashboard" ou Painel Integrado de Monitorização de Transportes Multimodais em Portugues que tem como objetivo a elaboração dum protótipo funcional de uma ferramenta para a monitorização de transportes multimodais.At present time there exist various intelligent systems in Transportation area that permit monitoring, control and other relevant functionalities for a given transport means. However, individual solutions for different transport means don't favor multimodal transport management; there are a very few solutions that combine different transport types in one application. Therefore, a need for a multimodal supervision dashboard arouse - a dashboard that would permit to combine transportation systems of different types and that would provide a comprehensive view in order to observe whether all the systems are functional and operating at an acceptable Level of Service (LOS). Since these systems supply services and data of different scope and varied detail and quality levels, the detection of an abnormal functioning of a certain transportation system is a challenge. It requires identification, application, adaptation or creation of metrics for each transportation system functioning. The metrics should be based on the data supplied by the protocols used by the ITSs integrated in the solution. This problem is addressed by the project "Integrated Multimodal Transportation Dashboard" and has as an aim the elaboration of a functional prototype of a tool for the monitoring of multimodal transports

Development and Performance Evaluation of Urban Mobility Applications and Services

L'abstract è presente nell'allegato / the abstract is in the attachmen

Development of virtual cities models during emergencies

L'abstract è presente nell'allegato / the abstract is in the attachmen

Aplicações de IoT no contexto de uma cidade inteligente

Over the last few years, Smart City solutions mature very rapidly alongside IoT and cloud computing. These technologies made it easier to create services and incorporate applications devoted to improving citizen’s quality of life and offer ways for businesses to implement their solutions. Through rapid advances in the quality of sensors, new methods emerged, combining different types of devices to create a better picture of the environment. The purpose of this dissertation is to provide useful information thought public services, that can be accessed by people visiting or residing in the beach area of Costa Nova and Barra. It also provides a solution for the traffic classification problem that projects based on radar data tend to face. These applications take advantage of the devices implemented in the PASMO project, such as parking sensors, radars, and CCTV cameras. By making the service public, businesses have the opportunity to build applications on top of it, utilizing the sensor data without being directly connected to the data storage. The example developed in this dissertation offers a dashboard experience where users can navigate through charts that provide a variety of data and real-time maps. It also provides a public API that researchers and businesses can use to develop new applications in the context of PASMO. The other area tackled in this document is traffic classification. Although the data provided is reliable for the most part, one big issue is the accuracy of vehicle classification provided by the radar. Still, this device offers precise values when it comes to detection, with the cameras doing a good job in classifying traffic. The goal is to combine these two devices to present much precise information, using state-of-the-art object detection algorithms and sensor fusion methods. In the end, the system will enrich the PASMO project by making its data easily available to the public while correcting the accuracy problems of some devices.Nos últimos anos, as soluções Smart City amadurecem muito rapidamente em conjunto com IoT e serviços na cloud. Estas tecnologias facilitam a criação de serviços e a incorporação de aplicações direcionados á melhoria da qualidade de vida do cidadão, oferecendo formas das empresas implementarem suas soluções. Por meio de rápidos avanços na qualidade dos sensores, novos métodos surgiram, combinando diferentes tipos de dispositivos para criar uma melhor imagem da realidade. O objetivo desta dissertação é fornecer informações úteis através de serviços públicos, que podem ser acedidos por pessoas que visitam ou residem na Costa Nova e Barra. Também fornece uma solução para o problema de classificação de tráfego que projetos baseados em dados de radar tendem a enfrentar. Estas aplicações beneficiam dos dispositivos implementados no projeto PASMO, como sensores de estacionamento, radares e câmeras de CFTV. Ao disponibilizar os serviços publicamente, as empresas têm a oportunidade de construir as suas próprias aplicações em cima destes, usando os dados dos sensores sem estar diretamente conectado ao armazenamento de dados. O exemplo desenvolvido nesta dissertação oferece uma experiência de dashboard onde os utilizadores podem navegar por gráficos que fornecem uma variedade de dados e mapas em tempo real. Também fornece uma API pública que os investigadores e empresas podem usar para desenvolver novos aplicativos no contexto do PASMO. A outra área abordada neste documento é a classificação de tráfego. Embora os dados fornecidos sejam confiáveis, um grande problema provém da precisão da classificação dos veículos fornecida pelo radar. Ainda assim, este dispositivo oferece valores precisos quando se trata de detecção, com as câmeras fazendo um bom trabalho na parte de classificação do tráfego. O objetivo é combinar estes dois dispositivos para apresentar informações corretas, usando algoritmos de detecção de objetos e métodos de fusão de sensores. No final, o sistema irá enriquecer o projeto PASMO, tornando seus dados facilmente disponíveis ao público e corrigindo problemas de precisão de alguns dispositivos.Mestrado em Engenharia de Computadores e Telemátic

EEG-based mental workload neurometric to evaluate the impact of different traffic and road conditions in real driving settings

Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart