Integrating Cloud Computing with Next-Generation Telematics for Energy Sustainability in Vehicular Networks

This research focuses on new approaches to enhance the economic viability of newer hybrid/electric vehicle technology utilizing a telematic and cloud computing framework. First, an economic foundation is proposed that rewards drivers for energy efficient driver behavior in units of energy based on a predefined standard. Next, a service model is presented that allows drivers to transfer information regarding their energy efficiency through a telematic and cloud computing network. Based on existing cloud computing technology and telematic standards, a network architecture is proposed to transfer this information to service integrators and content providers that can use this information to create vehicle energy resource management capabilities for vehicle users and fleet owners. Such an architecture would enable drivers or fleet owners to redeem energy units for monetary or promotional incentives, thereby realizing more economic value for the vehicle investment

Investigating Initial Driver Intention on Overtaking on Rural Roads

Driver intention recognition is essential to the development of advanced driver assistance systems providing real-time support. Current approaches for the recognition of overtaking intentions focus on drivers’ observable behaviors, neglecting the fact that the intention to overtake a slower lead car emerges earlier than the resulting behavior. This paper aims to distinguish the "intention emerging process", when drivers form the initial intention to overtake, from the "action executing process", when drivers execute the overtaking maneuver. A driving simulator study has been conducted to investigate the influence of the lead vehicle type and lead vehicle speed on initiating driver’ intention on overtaking on rural roads, and the effect of the complexity of the oncoming traffic on executing overtaking. The results show that the initial driver intention to overtake appears much earlier than the execution of the overtaking maneuver. The lead vehicle speed has a significant influence on initial driver intention in the "intention emerging process", while time to overtake increases with the number of the oncoming vehicles in the "action execution process". These results can contribute to the development of models for driver intention recognition by extending the prediction horizon from the recognition to a prediction of driving maneuvers. Document type: Conference objec

Big Data for All: Privacy and User Control in the Age of Analytics

We live in an age of “big data.” Data have become the raw material of production, a new source for immense economic and social value. Advances in data mining and analytics and the massive increase in computing power and data storage capacity have expanded by orders of magnitude the scope of information available for businesses and government. Data are now available for analysis in raw form, escaping the confines of structured databases and enhancing researchers’ abilities to identify correlations and conceive of new, unanticipated uses for existing information. In addition, the increasing number of people, devices, and sensors that are now connected by digital networks has revolutionized the ability to generate, communicate, share, and access data. Data creates enormous value for the world economy, driving innovation, productivity, efficiency, and growth. At the same time, the “data deluge” presents privacy concerns which could stir a regulatory backlash dampening the data economy and stifling innovation. In order to craft a balance between beneficial uses of data and individual privacy, policymakers must address some of the most fundamental concepts of privacy law, including the definition of “personally identifiable information,” the role of individual control, and the principles of data minimization and purpose limitation. This article emphasizes the importance of providing individuals with access to their data in usable format. This will let individuals share the wealth created by their information and incentivize developers to offer user-side features and applications harnessing the value of big data. Where individual access to data is impracticable, data are likely to be de-identified to an extent sufficient to diminish privacy concerns. In addition, since in a big data world it is often not the data but rather the inferences drawn from them that give cause for concern, organizations should be required to disclose their decisional criteria

Big Data for All: Privacy and User Control in the Age of Analytics

We live in an age of “big data.” Data have become the raw material of production, a new source for immense economic and social value. Advances in data mining and analytics and the massive increase in computing power and data storage capacity have expanded by orders of magnitude the scope of information available for businesses and government. Data are now available for analysis in raw form, escaping the confines of structured databases and enhancing researchers’ abilities to identify correlations and conceive of new, unanticipated uses for existing information. In addition, the increasing number of people, devices, and sensors that are now connected by digital networks has revolutionized the ability to generate, communicate, share, and access data. Data creates enormous value for the world economy, driving innovation, productivity, efficiency, and growth. At the same time, the “data deluge” presents privacy concerns which could stir a regulatory backlash dampening the data economy and stifling innovation. In order to craft a balance between beneficial uses of data and individual privacy, policymakers must address some of the most fundamental concepts of privacy law, including the definition of “personally identifiable information,” the role of individual control, and the principles of data minimization and purpose limitation. This article emphasizes the importance of providing individuals with access to their data in usable format. This will let individuals share the wealth created by their information and incentivize developers to offer user-side features and applications harnessing the value of big data. Where individual access to data is impracticable, data are likely to be deidentified to an extent sufficient to diminish privacy concerns. In addition, since in a big data world it is often not the data but rather the inferences drawn from them that give cause for concern, organizations should be required to disclose their decisional criteria

Use and citation of paper "Fox et al (2018), “When should the chicken cross the road? Game theory for autonomous vehicle - human interactions conference paper”" by the Law Commission to review and potentially change the law of the UK on autonomous vehicles. Cited in their consultation report, "Automated Vehicles: A joint preliminary consultation paper" on p174, ref 651.

Topic of this consultation: The Centre for Connected and Automated Vehicles (CCAV) has asked the Law Commission of England and Wales and the Scottish Law Commission to examine options for regulating automated road vehicles. It is a three-year project, running from March 2018 to March 2021. This preliminary consultation paper focuses on the safety of passenger vehicles. Driving automation refers to a broad range of vehicle technologies. Examples range from widely-used technologies that assist human drivers (such as cruise control) to vehicles that drive themselves with no human intervention. We concentrate on automated driving systems which do not need human drivers for at least part of the journey. This paper looks at are three key themes. First, we consider how safety can be assured before and after automated driving systems are deployed. Secondly, we explore criminal and civil liability. Finally, we examine the need to adapt road rules for artificial intelligence

GECAF : a generic and extensible framework for developing context-aware smart environments

The new pervasive and context-aware computing models have resulted in the development of modern environments which are responsive to the changing needs of the people who live, work or socialise in them. These are called smart envirnments and they employ high degree of intelligence to consume and process information in order to provide services to users in accordance with their current needs. To achieve this level of intelligence, such environments collect, store, represent and interpret a vast amount of information which describes the current context of their users. Since context-aware systems differ in the way they interact with users and interpret the context of their entities and the actions they need to take, each individual system is developed in its own way with no common architecture. This fact makes the development of every context aware system a challenge. To address this issue, a new and generic framework has been developed which is based on the Pipe-and-Filter software architectural style, and can be applied to many systems. This framework uses a number of independent components that represent the usual functions of any context-aware system. These components can be configured in different arrangements to suit the various systems' requirements. The framework and architecture use a model to represent raw context information as a function of context primitives, referred to as Who, When, Where, What and How (4W1H). Historical context information is also defined and added to the model to predict some actions in the system. The framework uses XML code to represent the model and describes the sequence in which context information is being processed by the architecture's components (or filters). Moreover, a mechanism for describing interpretation rules for the purpose of context reasoning is proposed and implemented. A set of guidelines is provided for both the deployment and rule languages to help application developers in constructing and customising their own systems using various components of the new framework. To test and demonstrate the functionality of the generic architecture, a smart classroom environment has been adopted as a case study. An evaluation of the new framework has also been conducted using two methods: quantitative and case study driven evaluation. The quantitative method used information obtained from reviewing the literature which is then analysed and compared with the new framework in order to verify the completeness of the framework's components for different xiisituations. On the other hand, in the case study method the new framework has been applied in the implementation of different scenarios of well known systems. This method is used for verifying the applicability and generic nature of the framework. As an outcome, the framework is proven to be extensible with high degree of reusability and adaptability, and can be used to develop various context-aware systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

GECAF : a generic and extensible framework for developing context-aware smart environments

The new pervasive and context-aware computing models have resulted in the development of modern environments which are responsive to the changing needs of the people who live, work or socialise in them. These are called smart envirnments and they employ high degree of intelligence to consume and process information in order to provide services to users in accordance with their current needs. To achieve this level of intelligence, such environments collect, store, represent and interpret a vast amount of information which describes the current context of their users. Since context-aware systems differ in the way they interact with users and interpret the context of their entities and the actions they need to take, each individual system is developed in its own way with no common architecture. This fact makes the development of every context aware system a challenge. To address this issue, a new and generic framework has been developed which is based on the Pipe-and-Filter software architectural style, and can be applied to many systems. This framework uses a number of independent components that represent the usual functions of any context-aware system. These components can be configured in different arrangements to suit the various systems' requirements. The framework and architecture use a model to represent raw context information as a function of context primitives, referred to as Who, When, Where, What and How (4W1H). Historical context information is also defined and added to the model to predict some actions in the system. The framework uses XML code to represent the model and describes the sequence in which context information is being processed by the architecture's components (or filters). Moreover, a mechanism for describing interpretation rules for the purpose of context reasoning is proposed and implemented. A set of guidelines is provided for both the deployment and rule languages to help application developers in constructing and customising their own systems using various components of the new framework. To test and demonstrate the functionality of the generic architecture, a smart classroom environment has been adopted as a case study. An evaluation of the new framework has also been conducted using two methods: quantitative and case study driven evaluation. The quantitative method used information obtained from reviewing the literature which is then analysed and compared with the new framework in order to verify the completeness of the framework's components for differentxiisituations. On the other hand, in the case study method the new framework has been applied in the implementation of different scenarios of well known systems. This method is used for verifying the applicability and generic nature of the framework. As an outcome, the framework is proven to be extensible with high degree of reusability and adaptability, and can be used to develop various context-aware systems