Intelligent transportation system and smart traffic flow with IOT

64-67There has been an increase in vehicles across the globe. Also, the congestion due to traffic has leapfrogged in India. The traffic flow information has been required to find out the route with minimum congestion and forecast the traffic. And this has been a part of the Intelligent Transportation System (ITS) which would help build smart cities. A lot of work has been done on the traffic measurement system. But the integration of emerging techniques such as the Internet of Things (IoT) and cloud computing has provided a lot of research scope in ITS. This paper has proposed an IoT-based method to determine the real-time traffic flow in a road section with ultrasonic sensors, Arduino, ESP8266 Wi-Fi module, and an open-source cloud. There has been an average traffic flow every five minutes to be displayed in the cloud platform. This method can be very much cost-effective with less power consumption and improved accuracy. Hence, the proposed IoT-based technique has provided the traffic flow data, and this data shall further be used for traffic predictions using machine learning algorithms

European cities in the face of sustainable development

Motivation: The paper rises the issue of sustainable development in the context of challenges that present cities face. Development of innovative and sustainable economy is an aim for the whole society in the European Union. Three pillars of sustainable development: economic growth, environmental management and social inclusion are fundamental and occur across all economic sectors. Predominantly, they influence cities e.g. fast urbanization process, infrastructure, energy development as well as transportation. Cities in Europe are precursors of the transformation towards a low carbon and resource efficient economy. City authorities are planning and acting towards a more sustainable future characterized by investing in innovative, integrated technologies and services such as buildings, mobility, lighting and broadband communications.Aim: The aim of the article is to present challenges facing European cities in the light of sustainable development. The paper explores trends in regional policy undertaken by the European Union relating to sustainable development. The first part outlines the global vision of present and future goals of sustainable development in the world. The next part focuses on the cities and challenges they face referring to sustainable and smart development.Results: The conducted research shows sustainable development as still valid, important and topical. The research indicates that the structures of the EU are not the exclusively supportive and responsible for sustainable development in Europe. The entities such as cities play a very important role in achieving sustainability. Numerous European cities are still establishing programs integrating the energy, transport and ICT sectors in order to deliver more efficient services to their inhabitants. Finally, more and more cities are going to become smart in the nearest future

Are You Responsible for Traffic Congestion? A Systematic Review of the Socio-technical Perspective of Smart Mobility Services

A large amount of the pollution of modern cities is caused by individual transportation. Hence, many road users suffer from stress, emissions and noise. Smart mobility services can help improving the situa-tion by distributing traffic more consistently across different routes, times, and transportation modes. These services comprise two dimensions, a technical and a socio-technical. The latter addresses the road user’s role as data and knowledge provider and stresses the road user’s role in actively contributing to relieved traffic. As such, road users display one of the strongest levers to sustainably relieve traffic both in terms of knowledge providers and traffic actors. Using a systematic analysis of 28 publications, we show that existing SMob services show several chal-lenges related to the involvement of road users. We call for more research on SMob services that account for long-term user involvement e.g. by positively in-fluences road users’ practices and routines

Automatic Transportation Mode Recognition on Smartphone Data Based on Deep Neural Networks

In the last few years, with the exponential diffusion of smartphones, services for turn-by-turn navigation have seen a surge in popularity. Current solutions available in the market allow the user to select via an interface the desired transportation mode, for which an optimal route is then computed. Automatically recognizing the transportation system that the user is travelling by allows to dynamically control, and consequently update, the route proposed to the user. Such a dynamic approach is an enabling technology for multi-modal transportation planners, in which the optimal path and its associated transportation solutions are updated in real-time based on data coming from (i) distributed sensors (e.g., smart traffic lights, road congestion sensors, etc.); (ii) service providers (e.g., car-sharing availability, bus waiting time, etc.); and (iii) the user’s own device, in compliance with the development of smart cities envisaged by the 5G architecture. In this paper, we present a series of Machine Learning approaches for real-time Transportation Mode Recognition and we report their performance difference in our field tests. Several Machine Learning-based classifiers, including Deep Neural Networks, built on both statistical feature extraction and raw data analysis are presented and compared in this paper; the result analysis also highlights which features are proven to be the most informative ones for the classification

Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving

Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures. Although reinforcement learning (RL)-based decision-making scheme is promising to handle urban driving scenarios, it suffers from low sample efficiency and poor adaptability. In this paper, we propose Scene-Rep Transformer to improve the RL decision-making capabilities with better scene representation encoding and sequential predictive latent distillation. Specifically, a multi-stage Transformer (MST) encoder is constructed to model not only the interaction awareness between the ego vehicle and its neighbors but also intention awareness between the agents and their candidate routes. A sequential latent Transformer (SLT) with self-supervised learning objectives is employed to distill the future predictive information into the latent scene representation, in order to reduce the exploration space and speed up training. The final decision-making module based on soft actor-critic (SAC) takes as input the refined latent scene representation from the Scene-Rep Transformer and outputs driving actions. The framework is validated in five challenging simulated urban scenarios with dense traffic, and its performance is manifested quantitatively by the substantial improvements in data efficiency and performance in terms of success rate, safety, and efficiency. The qualitative results reveal that our framework is able to extract the intentions of neighbor agents to help make decisions and deliver more diversified driving behaviors

European cities in the face of sustainable development

Motivation: The paper rises the issue of sustainable development in the context of challenges that present cities face. Development of innovative and sustainable economy is an aim for the whole society in the European Union. Three pillars of sustainable development: economic growth, environmental management and social inclusion are fundamental and occur across all economic sectors. Predominantly, they influence cities e.g. fast urbanization process, infrastructure, energy development as well as transportation. Cities in Europe are precursors of the transformation towards a low carbon and resource efficient economy. City authorities are planning and acting towards a more sustainable future characterized by investing in innovative, integrated technologies and services such as buildings, mobility, lighting and broadband communications.Aim: The aim of the article is to present challenges facing European cities in the light of sustainable development. The paper explores trends in regional policy undertaken by the European Union relating to sustainable development. The first part outlines the global vision of present and future goals of sustainable development in the world. The next part focuses on the cities and challenges they face referring to sustainable and smart development.Results: The conducted research shows sustainable development as still valid, important and topical. The research indicates that the structures of the EU are not the exclusively supportive and responsible for sustainable development in Europe. The entities such as cities play a very important role in achieving sustainability. Numerous European cities are still establishing programs integrating the energy, transport and ICT sectors in order to deliver more efficient services to their inhabitants. Finally, more and more cities are going to become smart in the nearest future.</p

Visibility-Based Technologies and Methodologies for Autonomous Driving

The three main elements of autonomous vehicles (AV) are orientation, visibility, and decision. This chapter presents an overview of the implementation of visibility-based technologies and methodologies. The chapter first presents two fundamental aspects that are necessary for understanding the main contents. The first aspect is highway geometric design as it relates to sight distance and highway alignment. The second aspect is mathematical basics, including coordinate transformation and visual space segmentation. Details on the Light Detection and Ranging (Lidar) system, which represents the ‘eye’ of the AV are presented. In particular, a new Lidar 3D mapping system, that can be operated on different platforms and modes for a new mapping scheme is described. The visibility methodologies include two types. Infrastructure visibility mainly addresses high-precision maps and sight obstacle detection. Traffic visibility (vehicles, pedestrians, and cyclists) addresses identification of critical positions and visibility estimation. Then, an overview of the decision element (path planning and intelligent car-following) for the movement of AV is presented. The chapter provides important information for researchers and therefore should help to advance road safety for autonomous vehicles

Inference of Non-Overlapping Camera Network Topology using Statistical Approaches

This work proposes an unsupervised learning model to infer the topological information of a camera network automatically. This algorithm works on non-overlapped and overlapped cameras field of views (FOVs). The constructed model detects the entry/exit zones of the moving objects across the cameras FOVs using the Data-Spectroscopic method. The probabilistic relationships between each pair of entry/exit zones are learnt to localize the camera network nodes. Increase the certainty of the probabilistic relationships using Computer-Generating to create more Monte Carlo observations of entry/exit points. Our method requires no assumptions, no processors for each camera and no communication among the cameras. The purpose is to figure out the relationship between each pair of linked cameras using the statistical approaches which help to track the moving objects depending on their present location. The Output is shown as a Markov chain model that represents the weighted-unit links between each pair of cameras FOV

Exploration of smart infrastructure for drivers of autonomous vehicles

The connection between vehicles and infrastructure is an integral part of providing autonomous vehicles information about the environment. Autonomous vehicles need to be safe and users need to trust their driving decision. When smart infrastructure information is integrated into the vehicle, the driver needs to be informed in an understandable manner what the smart infrastructure detected. Nevertheless, interactions that benefit from smart infrastructure have not been the focus of research, leading to knowledge gaps in the integration of smart infrastructure information in the vehicle. For example, it is unclear, how the information from two complex systems can be presented, and if decisions are made, how these can be explained. Enriching the data of vehicles with information from the infrastructure opens unexplored opportunities. Smart infrastructure provides vehicles with information to predict traffic flow and traffic events. Additionally, it has information about traffic events in several kilometers distance and thus enables a look ahead on a traffic situation, which is not in the immediate view of drivers. We argue that this smart infrastructure information can be used to enhance the driving experience. To achieve this, we explore designing novel interactions, providing warnings and visualizations about information that is out of the view of the driver, and offering explanations for the cause of changed driving behavior of the vehicle. This thesis focuses on exploring the possibilities of smart infrastructure information with a focus on the highway. The first part establishes a design space for 3D in-car augmented reality applications that profit from smart infrastructure information. Through the input of two focus groups and a literature review, use cases are investigated that can be introduced in the vehicle's interaction interface which, among others, rely on environment information. From those, a design space that can be used to design novel in-car applications is derived. The second part explores out-of-view visualizations before and during take over requests to increase situation awareness. With three studies, different visualizations for out-of-view information are implemented in 2D, stereoscopic 3D, and augmented reality. Our results show that visualizations improve the situation awareness about critical events in larger distances during take over request situations. In the third part, explanations are designed for situations in which the vehicle drives unexpectedly due to unknown reasons. Since smart infrastructure could provide connected vehicles with out-of-view or cloud information, the driving maneuver of the vehicle might remain unclear to the driver. Therefore, we explore the needs of drivers in those situations and derive design recommendations for an interface which displays the cause for the unexpected driving behavior. This thesis answers questions about the integration of environment information in vehicles'. Three important aspects are explored, which are essential to consider when implementing use cases with smart infrastructure in mind. It enables to design novel interactions, provides insights on how out-of-view visualizations can improve the drivers' situation awareness and explores unexpected driving situations and the design of explanations for them. Overall, we have shown how infrastructure and connected vehicle information can be introduced in vehicles' user interface and how new technology such as augmented reality glasses can be used to improve the driver's perception of the environment.Autonome Fahrzeuge werden immer mehr in den alltäglichen Verkehr integriert. Die Verbindung von Fahrzeugen mit der Infrastruktur ist ein wesentlicher Bestandteil der Bereitstellung von Umgebungsinformationen in autonome Fahrzeugen. Die Erweiterung der Fahrzeugdaten mit Informationen der Infrastruktur eröffnet ungeahnte Möglichkeiten. Intelligente Infrastruktur übermittelt verbundenen Fahrzeugen Informationen über den prädizierten Verkehrsfluss und Verkehrsereignisse. Zusätzlich können Verkehrsgeschehen in mehreren Kilometern Entfernung übermittelt werden, wodurch ein Vorausblick auf einen Bereich ermöglicht wird, der für den Fahrer nicht unmittelbar sichtbar ist. Mit dieser Dissertation wird gezeigt, dass Informationen der intelligenten Infrastruktur benutzt werden können, um das Fahrerlebnis zu verbessern. Dies kann erreicht werden, indem innovative Interaktionen gestaltet werden, Warnungen und Visualisierungen über Geschehnisse außerhalb des Sichtfelds des Fahrers vermittelt werden und indem Erklärungen über den Grund eines veränderten Fahrzeugverhaltens untersucht werden. Interaktionen, welche von intelligenter Infrastruktur profitieren, waren jedoch bisher nicht im Fokus der Forschung. Dies führt zu Wissenslücken bezüglich der Integration von intelligenter Infrastruktur in das Fahrzeug. Diese Dissertation exploriert die Möglichkeiten intelligenter Infrastruktur, mit einem Fokus auf die Autobahn. Der erste Teil erstellt einen Design Space für Anwendungen von augmentierter Realität (AR) in 3D innerhalb des Autos, die unter anderem von Informationen intelligenter Infrastruktur profitieren. Durch das Ergebnis mehrerer Studien werden Anwendungsfälle in einem Katalog gesammelt, welche in die Interaktionsschnittstelle des Autos einfließen können. Diese Anwendungsfälle bauen unter anderem auf Umgebungsinformationen. Aufgrund dieser Anwendungen wird der Design Space entwickelt, mit Hilfe dessen neuartige Anwendungen für den Fahrzeuginnenraum entwickelt werden können. Der zweite Teil exploriert Visualisierungen für Verkehrssituationen, die außerhalb des Sichtfelds des Fahrers sind. Es wird untersucht, ob durch diese Visualisierungen der Fahrer besser auf ein potentielles Übernahmeszenario vorbereitet wird. Durch mehrere Studien wurden verschiedene Visualisierungen in 2D, stereoskopisches 3D und augmentierter Realität implementiert, die Szenen außerhalb des Sichtfelds des Fahrers darstellen. Diese Visualisierungen verbessern das Situationsbewusstsein über kritische Szenarien in einiger Entfernung während eines Übernahmeszenarios. Im dritten Teil werden Erklärungen für Situationen gestaltet, in welchen das Fahrzeug ein unerwartetes Fahrmanöver ausführt. Der Grund des Fahrmanövers ist dem Fahrer dabei unbekannt. Mit intelligenter Infrastruktur verbundene Fahrzeuge erhalten Informationen, die außerhalb des Sichtfelds des Fahrers liegen oder von der Cloud bereit gestellt werden. Dadurch könnte der Grund für das unerwartete Fahrverhalten unklar für den Fahrer sein. Daher werden die Bedürfnisse des Fahrers in diesen Situationen erforscht und Empfehlungen für die Gestaltung einer Schnittstelle, die Erklärungen für das unerwartete Fahrverhalten zur Verfügung stellt, abgeleitet. Zusammenfassend wird gezeigt wie Daten der Infrastruktur und Informationen von verbundenen Fahrzeugen in die Nutzerschnittstelle des Fahrzeugs implementiert werden können. Zudem wird aufgezeigt, wie innovative Technologien wie AR Brillen, die Wahrnehmung der Umgebung des Fahrers verbessern können. Durch diese Dissertation werden Fragen über Anwendungsfälle für die Integration von Umgebungsinformationen in Fahrzeugen beantwortet. Drei wichtige Themengebiete wurden untersucht, welche bei der Betrachtung von Anwendungsfällen der intelligenten Infrastruktur essentiell sind. Durch diese Arbeit wird die Gestaltung innovativer Interaktionen ermöglicht, Einblicke in Visualisierungen von Informationen außerhalb des Sichtfelds des Fahrers gegeben und es wird untersucht, wie Erklärungen für unerwartete Fahrsituationen gestaltet werden können