Download or read book Autonomous Driving Network written by Wenshuan Dang and published by CRC Press. This book was released on 2024-01-17 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aiming to outline the vision of realizing automated and intelligent communication networks in the era of intelligence, this book describes the development history, application scenarios, theories, architectures, and key technologies of Huawei's Autonomous Driving Network (ADN) solution. In the book, the authors explain the design of the top-level architecture, hierarchical architecture (ANE, NetGraph, and AI Native NE), and key feature architecture (distributed AI and endogenous security) that underpin Huawei's ADN solution. The book delves into various key technologies, including trustworthy AI, distributed AI, digital twin, network simulation, digitization of knowledge and expertise, human-machine symbiosis, NE endogenous intelligence, and endogenous security. It also provides an overview of the standards and level evaluation methods defined by industry and standards organizations, and uses Huawei's ADN solution as an example to illustrate how to implement AN. This book is an essential reference for professionals and researchers who want to gain a deeper understanding of automated and intelligent communication networks and their applications.

Download or read book Applied Deep Learning and Computer Vision for Self Driving Cars written by Sumit Ranjan and published by Packt Publishing Ltd. This book was released on 2020-08-14 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV Key FeaturesBuild and train powerful neural network models to build an autonomous carImplement computer vision, deep learning, and AI techniques to create automotive algorithmsOvercome the challenges faced while automating different aspects of driving using modern Python libraries and architecturesBook Description Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries. What you will learnImplement deep neural network from scratch using the Keras libraryUnderstand the importance of deep learning in self-driving carsGet to grips with feature extraction techniques in image processing using the OpenCV libraryDesign a software pipeline that detects lane lines in videosImplement a convolutional neural network (CNN) image classifier for traffic signal signsTrain and test neural networks for behavioral-cloning by driving a car in a virtual simulatorDiscover various state-of-the-art semantic segmentation and object detection architecturesWho this book is for If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.

Download or read book Signal and Information Processing Networking and Computers written by Yue Wang and published by Springer Nature. This book was released on 2023-02-23 with total page 1257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects selected papers from the 10th Conference on Signal and Information Processing, Networking and Computers held in Xi’Ning, China held in July, 2022. The book focuses on the current works of information theory, communication system, computer science, aerospace technologies and big data and other related technologies. People from both academia and industry of this field can contribute and find their interests from the book.

Download or read book Robot Learning written by J. H. Connell and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.

Download or read book Campus Network Architectures and Technologies written by Ningguo Shen and published by CRC Press. This book was released on 2021-05-05 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Campus Network Architectures and Technologies begins by describing the service challenges facing campus networks, and then details the intent-driven campus network architectures and technologies of Huawei Cloud Campus Solution. After reading this book, you will have a comprehensive understanding of next-generation campus network solutions, technical implementations, planning, design, and other know-how. Leveraging Huawei’s years of technical expertise and practices in the campus network field, this book systematically describes the use of technical solutions such as virtualization, big data, AI, and SDN in campus networks. You will be able to reconstruct campus networks quickly and efficiently utilizing this informative description. Additionally, this book provides detailed suggestions for campus network design and deployment based on Huawei's extensive project implementation experience, assisting with the construction of automated and intelligent campus networks required to cope with challenges. This is a practical, informative, and easy-to-understand guide for learning about and designing campus networks. It is intended for network planning engineers, network technical support engineers, network administrators, and enthusiasts of campus network technologies. Authors Ningguo Shen is Chief Architect for Huawei’s campus network solutions. He has approximately 20 years' experience in campus network product and solution design, as well as a wealth of expertise in network planning and design. Mr. Shen previously served as a system engineer for the campus switch, data center switch, and WLAN product lines, and led the design of Huawei’s intent-driven campus network solution. Bin Yu is an Architect for Huawei's campus network solutions. He has 12 years’ experience in campus network product and solution design, as well as extensive expertise in network planning and design and network engineering project implementation. Mr. Yu once led the design of multiple features across various campus network solutions. Mingxiang Huang is a Documentation Engineer for Huawei’s campus network solutions. He has three years of technical service experience, and four years of expertise in developing campus network product documentation. Mr. Huang was previously in charge of writing manuals for Huawei router and switch products. He has authored many popular technical series, including Be an OSPF Expert, Insight into Routing Policies, and Story behind Default Routes. Hailin Xu is a Documentation Engineer for Huawei’s campus network solutions. He has two years of marketing experience in smart campus solutions, and six years of expertise in developing network products and solution documentation. Extremely familiar with Huawei's campus network products and solutions, Mr. Xu was previously in charge of writing manuals for Huawei routers, switches, and campus network solutions. In addition, he has participated in smart campus marketing projects within such sectors as education, government, and real estate.

Download or read book The 5G Myth written by William Webb and published by Deg Press. This book was released on 2018 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5G Myth explains why the vision of 5G, the next generation in mobile telephony, heralded as a huge advance in global connectivity, is flawed and sets out a better vision for a connected future. It explains why insufficient technological advances and inadequate profitability will be problems in the widespread implementation of 5G. The book advocates a focus on consistent connectivity everywhere rather than fast speeds in city centers. William Webb looks back at the transitions through previous generations of mobile telephony and shows what simple extrapolations of trends would predict for 5G. He discusses whether the increases in speed and capacity promised by 5G are needed; if the required technology is available; whether a sound business case can be made for the deployment; and asks why, given this, the industry appears so supportive of 5G. He then puts forth the argument in favor of consistent connectivity of around 10Mbits/s everywhere as a more compelling vision and shows how it can be delivered via a mix of 4G and Wi-Fi. Subscribers to The Economist can access an article featuring this book at https: //www.economist.com/business/2019/08/24/vodafones-search-for-the-g-spot

Download or read book Cellular V2X for Connected Automated Driving written by Mikael Fallgren and published by John Wiley & Sons. This book was released on 2021-04-19 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: CELLULAR V2X FOR CONNECTED AUTOMATED DRIVING A unique examination of cellular communication technologies for connected automated driving, combining expert insights from telecom and automotive industries as well as technical and scientific knowledge from industry and academia Cellular vehicle-to-everything (C-V2X) technologies enable vehicles to communicate both with the network, with each other, and with other road users using reliable, responsive, secure, and high-capacity communication links. Cellular V2X for Connected Automated Driving provides an up-to-date view of the role of C-V2X technologies in connected automated driving (CAD) and connected road user (CRU) services, such as advanced driving support, improved road safety, infotainment, over-the-air software updates, remote driving, and traffic efficiency services enabling the future large-scale transition to self-driving vehicles. This timely book discusses where C-V2X technology is situated within the increasingly interconnected ecosystems of the mobile communications and automotive industries. An expert contributor team from both industry and academia explore potential applications, business models, standardization, spectrum and channel modelling, network enhancements, security and privacy, and more. Broadly divided into two parts—introductory and advanced material—the text first introduces C-V2X technology and introduces a variety of use cases and opportunities, requiring no prerequisite technical knowledge. The second part of the book assumes a basic understanding of the field of telecommunications, presenting technical descriptions of the radio, system aspects, and network design for the previously discussed applications. This up-to-date resource: Provides technical details from the finding of the European Commission H2020 5G PPP 5GCAR project, a collaborative research initiative between the telecommunications and automotive industries and academic researchers Elaborates on use cases, business models, and a technology roadmap for those seeking to shape a start-up in the area of automated and autonomous driving Provides up to date descriptions of standard specifications, standardization and industry organizations and important regulatory aspects for connected vehicles Provides technical insights and solutions for the air interface, network architecture, positioning and security to support vehicles at different automation levels Includes detailed tables, plots, and equations to clarify concepts, accompanied by online tutorial slides for use in teaching and seminars Thanks to its mix of introductory content and technical information, Cellular V2X for Connected Automated Driving is a must-have for industry and academic researchers, telecom and automotive industry practitioners, leaders, policymakers, and regulators, and university-level instructors and students. Additional resources available at the following site: Cellular V2X for Connected Automated Driving – 5GCAR

Download or read book Machine Learning Empowered Intelligent Data Center Networking written by Ting Wang and published by Springer Nature. This book was released on 2023-02-21 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to the Machine Learning Empowered Intelligent Data Center Networking Fundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks. Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security. Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.

Download or read book Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Download or read book Autonomous Driving written by Markus Maurer and published by Springer. This book was released on 2016-05-21 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".

Download or read book Complex Networks and Their Applications VII written by Luca Maria Aiello and published by Springer. This book was released on 2018-12-05 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Download or read book Deep Neural Networks and Data for Automated Driving written by Tim Fingscheidt and published by Springer Nature. This book was released on 2022-07-19 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Download or read book Autonomous Vehicles written by George Dimitrakopoulos and published by Elsevier. This book was released on 2021-04-15 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Vehicles: Technologies, Regulations, and Societal Impacts explores both the autonomous driving concepts and the key hardware and software enablers, Artificial intelligence tools, needed infrastructure, communication protocols, and interaction with non-autonomous vehicles. It analyses the impacts of autonomous driving using a scenario-based approach to quantify the effects on the overall economy and affected sectors. The book assess from a qualitative and quantitative approach, the future of autonomous driving, and the main drivers, challenges, and barriers. The book investigates whether individuals are ready to use advanced automated driving vehicles technology, and to what extent we as a society are prepared to accept highly automated vehicles on the road. Building on the technologies, opportunities, strengths, threats, and weaknesses, Autonomous Vehicles: Technologies, Regulations, and Societal Impacts discusses the needed frameworks for automated vehicles to move inside and around cities. The book concludes with a discussion on what in applications comes next, outlining the future research needs. - Broad, interdisciplinary and systematic coverage of the key issues in autonomous driving and vehicles - Examines technological impact on society, governance, and the economy as a whole - Includes foundational topical coverage, case studies, objectives, and glossary

Download or read book Intelligent Multi Modal Data Processing written by Soham Sarkar and published by John Wiley & Sons. This book was released on 2021-04-06 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal Data Processing contains a review of the most recent applications of data processing. The Editors and contributors – noted experts on the topic – offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-processing and/or post-processing technique for model building. The book also contains images that show the efficiency of the algorithm on standard data set. This important book: Includes an in-depth analysis of the state-of-the-art applications of signal and data processing Contains contributions from noted experts in the field Offers information on hybrid differential evolution for optimal multilevel image thresholding Presents a fuzzy decision based multi-objective evolutionary method for video summarisation Written for students of technology and management, computer scientists and professionals in information technology, Intelligent Multi-Modal Data Processing brings together in one volume the range of multi-modal data processing.

Download or read book Autonomous Vehicle Technology written by James M. Anderson and published by Rand Corporation. This book was released on 2014-01-10 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.

Download or read book Autonomous Driving Changes the Future written by Zhanxiang Chai and published by Springer Nature. This book was released on 2020-07-22 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically discusses the development of autonomous driving, describing the related history, technological advances, infrastructure, social impacts, international competition, China’s opportunities and challenges, and possible future scenarios. This popular science book uses straightforward language and includes quotes from ancient Chinese poems to enhance the reading experience. The discussions are supplemented by theoretical elaborations, presented in tables and figures. The book is intended for auto fans, upper undergraduate and graduate students in the field of automotive engineering.

Download or read book Autonorama written by Peter Norton and published by Island Press. This book was released on 2021-10-21 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Autonorama: The Illusory Promise of High-Tech Driving, historian Peter Norton argues that driverless cars cannot be the safe, sustainable, and inclusive "mobility solutions" that tech companies and automakers are promising us. The salesmanship behind the "driverless future" is distracting us from better ways to get around that we can implement now. Unlike autonomous vehicles, these alternatives are inexpensive, safe, sustainable, and inclusive. Norton takes the reader on an engaging ride--from the GM Futurama exhibit to "smart" highways and vehicles--to show how we are once again being sold car dependency in the guise of mobility. Autonorama is hopeful, advocating for wise, proven, humane mobility that we can invest in now, without waiting for technology that is forever just out of reach.