A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.Comment: 29 pages, 7 figures, 2 tables. IEEE Open Journal of the Communications Society (2022

Collaborative Sensing in Automotive Scenarios : Enhancement of the Vehicular Electronic Horizon through Collaboratively Sensed Knowledge

Modern vehicles are equipped with a variety of advanced driver assistance systems that increase driving comfort, economy and safety. Respective information sources for these systems are local sensors, like cameras, radar or lidar. However, the next generation of assistant systems will require information above the local sensing range. An extension of the local perception can be provided by the use of appro- priate communication mechanisms. Hence, other vehicles can serve as an informa- tion source by providing their local perception data, but also any other information source, such as cloud services. Required communication can take place directly be- tween vehicles via mobile ad-hoc communication or via a backend by the use of cellu- lar communication. The appropriate technology depends on the respective use case, that determines information content, granularity and tolerated latency. Based on liter- ature, we derived a categorization of use case dependent information demands, with respect to communication. The resulting three zones, namely safety zone, awareness zone and information zone, refer to the tolerated latency between the occurrence of an information and the point in time the information has to be processed at the receiver side. While communication mechanisms for the safety zone, i. e., the ego-vehicle’s di- rect surroundings with a remaining driving time of less than 2 − 5 seconds, have been focus in research and standardization in the past, respective mechanisms for larger distances have not been sufficiently considered. In this thesis, we examine in- formation distribution mechanisms in context of the previously mentioned use case categories. As the first key contribution, we consider the gathering of vehicular sensed data with regard to the information zone, i. e., more than 30 seconds remaining driving time to the point of the information origin. We developed a probabilistic data collection model that is able to reduce data traffic up to 85 % compared to opportunistic trans- mission and still sticks to certain quality metrics, e. g., a maximum detection latency. A central adaption of transmission probabilities to the density of transmitting vehi- cles is applicable for cellular use and copes with sparse traffic situations. Moreover, we have extended this approach by hybrid communication, i. e., the parallel use of cellular and mobile ad-hoc communication. This allows to further reduce cellular based data traffic, in particular in case of dense traffic. As the second key contribution, we examine the efficient distribution of the pre- viously gathered information. Information is structured and prioritized according to the most probable driving path, as so-called electronic horizon. The transmission towards the vehicles is performed in small data packets, according to the given pri- orities. The aim is to transmit only information relevant for road segments that will be used. Concerning this, we developed a mechanism for most probable travel path estimation and a data structure for efficient mapping of the electronic horizon. As the third key contribution, we examine the information exchange in the aware- ness zone, an area between the safety zone and the information zone with about 5 to 30 seconds remaining driving time to the point of the information origin. Derived from the respective use cases, this data is not directly safety relevant, but it is still about dynamic position information of neighboring vehicles. Due to the relatively long distance, direct vehicle to vehicle communication is not possible. Respective data has to be forwarded by intermediate vehicles. However, position beacons with- out data forwarding can already cause channel congestion in dense traffic situations. The use of cellular networks would require absolute total network coverage with permanent free channel resources. To enable forwarding of dynamic vehicle infor- mation anyhow, we developed at first a mechanism to reduce the channel load for position beacons. Next, we use the freed-up bandwidth to forward dynamic informa- tion about neighboring vehicle positions. With this mechanism, we are able to more than double the range of vehicular perception, with respect to moving objects. In extension to standardized communication mechanisms for the safety relevant direct proximity, our three mentioned contributions provide the means to complete the long range vehicular perception for future advanced driver assistance systems

Visual Privacy Mitigation Strategies in Social Media Networks and Smart Environments

The contemporary use of technologies and environments has led to a vast collection and sharing of visual data, such as images and videos. However, the increasing popularity and advancements in social media platforms and smart environments have posed a significant challenge in protecting the privacy of individuals’ visual data, necessitating a better understanding of the visual privacy implications in these environments. These concerns can arise intentionally or unintentionally from the individual, other entities in the environment, or a company. To address these challenges, it is necessary to inform the design of the data collection process and deployment of the system by understanding the visual privacy implications of these environments. However, ensuring visual privacy in social media networks and smart environments presents significant research challenges. These challenges include accounting for an individual’s subjectivity towards visual privacy, the influence of visual privacy leakage in the environment, and the environment’s infrastructure design and ownership. This dissertation employs a range of methodologies, including user studies, machine learning, and statistics to explore social media networks and smart environments and their visual privacy risks. Qualitative and quantitative studies were conducted to understand privacy perspectives in social media networks and smart city environments. The findings reveal that individuals and stakeholders possess inherited bias and subjectivity when considering privacy in these environments, leading to a need for visual privacy mitigation and risk analysis. Furthermore, a new visual privacy risk score using visual features and computer vision is developed to investigate and discover visual privacy leakage. However, using computer vision methods for visual privacy mitigation introduces additional privacy and fairness risks while developing and deploying visual privacy systems and machine learning algorithms. This necessitates the creation of interactive audit strategies to consider the broader impacts of research on the community. Overall, this dissertation contributes to advancing visual privacy solutions in social media networks and smart environments by investigating xiii and quantifying the visual privacy concerns and perspectives of individuals and stakeholders, advocating for the need for responsible visual privacy mitigation methods in these environments. It also strengthens the ability of researchers, stakeholders, and companies to protect individuals from visual privacy risks throughout the machine learning pipeline

Environment 2.0 : the 9th Biennial Conference on Environmental Psychology, 26-28 September 2011, Eindhoven University of Technology, Eindhoven, The Netherlands

On behalf of the Environmental Psychology Division of the German Association of Psychology, the 9th Biennial International Conference on Environmental Psychology is organized by the Human-Technology Interaction (HTI) group of the School of Innovation Sciences of the Eindhoven University of Technology. The HTI group is internationally acclaimed for perception research, and has become established as a major centre of excellence in human-technology interaction research. Bringing together psychological and engineering expertise, its central mission is investigating and optimizing interactions between people, systems, and environments, in the service of a socially and ecologically sustainable society

FinBook: literary content as digital commodity

This short essay explains the significance of the FinBook intervention, and invites the reader to participate. We have associated each chapter within this book with a financial robot (FinBot), and created a market whereby book content will be traded with financial securities. As human labour increasingly consists of unstable and uncertain work practices and as algorithms replace people on the virtual trading floors of the worlds markets, we see members of society taking advantage of FinBots to invest and make extra funds. Bots of all kinds are making financial decisions for us, searching online on our behalf to help us invest, to consume products and services. Our contribution to this compilation is to turn the collection of chapters in this book into a dynamic investment portfolio, and thereby play out what might happen to the process of buying and consuming literature in the not-so-distant future. By attaching identities (through QR codes) to each chapter, we create a market in which the chapter can ‘perform’. Our FinBots will trade based on features extracted from the authors’ words in this book: the political, ethical and cultural values embedded in the work, and the extent to which the FinBots share authors’ concerns; and the performance of chapters amongst those human and non-human actors that make up the market, and readership. In short, the FinBook model turns our work and the work of our co-authors into an investment portfolio, mediated by the market and the attention of readers. By creating a digital economy specifically around the content of online texts, our chapter and the FinBook platform aims to challenge the reader to consider how their personal values align them with individual articles, and how these become contested as they perform different value judgements about the financial performance of each chapter and the book as a whole. At the same time, by introducing ‘autonomous’ trading bots, we also explore the different ‘network’ affordances that differ between paper based books that’s scarcity is developed through analogue form, and digital forms of books whose uniqueness is reached through encryption. We thereby speak to wider questions about the conditions of an aggressive market in which algorithms subject cultural and intellectual items – books – to economic parameters, and the increasing ubiquity of data bots as actors in our social, political, economic and cultural lives. We understand that our marketization of literature may be an uncomfortable juxtaposition against the conventionally-imagined way a book is created, enjoyed and shared: it is intended to be