The End of Driving, By Bern Grush, Founder, Urban Robotics Foundation,
Старое издание
Автор: Grush, Bern (grush Niles Strategic) Niles, John (center For Advanced Transportation And Energy Solutions, Seattle, Wa, Usa) Название: End of driving ISBN: 0128154519 ISBN-13(EAN): 9780128154519 Издательство: Elsevier Science Цена: 16161.00 р. Наличие на складе: Есть у поставщикаПоставка под заказ. Описание:
While many transportation and city planners, researchers, students, practitioners, and political leaders are familiar with the technical nature and promise of vehicle automation, consensus is not yet often seen on the impact that will result, or the policies and actions that those responsible for transportation systems should take.
The End of Driving: Transportation Systems and Public Policy Planning for Autonomous Vehicles explores both the potential of vehicle automation technology and the barriers it faces when considering coherent urban deployment. The book evaluates the case for deliberate development of automated public transportation and mobility-as-a-service as paths towards sustainable mobility, describing critical approaches to the planning and management of vehicle automation technology. It serves as a reference for understanding the full life cycle of the multi-year transportation systems planning processes, including novel regulation, planning, and acquisition tools for regional transportation.
Application-oriented, research-based, and solution-oriented rather than predict-and-warn, The End of Driving concludes with a detailed discussion of the systems design needed for accomplishing this shift.
From the Foreword by Susan Shaheen: The authors ... extend potential solutions through a set of open-ended exercises after each chapter. Their approach is both strategic and deliberate. They lead the reader from definitions and context setting to the transition toward automation, employing a range of creative strategies and policies. While our quest to understand how to deploy automated vehicles is just beginning, this book provides a thoughtful introduction to inform this evolution.
Offers a workable public transit solution design melding the traditional "acquire-and-operate" mode with the absorption of new technology
Provides a step-by-step discussion of digital systems designs and effective regulation-by-data approaches needed for a new urban mobility
Learning aids include case study scenarios, chapter objectives and discussion questions, sidebars and a glossary
Автор: Grush, Bern (grush Niles Strategic) Niles, John (center For Advanced Transportation And Energy Solutions, Seattle, Wa, Usa) Название: End of driving ISBN: 0128154519 ISBN-13(EAN): 9780128154519 Издательство: Elsevier Science Рейтинг: Цена: 16161.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
While many transportation and city planners, researchers, students, practitioners, and political leaders are familiar with the technical nature and promise of vehicle automation, consensus is not yet often seen on the impact that will result, or the policies and actions that those responsible for transportation systems should take.
The End of Driving: Transportation Systems and Public Policy Planning for Autonomous Vehicles explores both the potential of vehicle automation technology and the barriers it faces when considering coherent urban deployment. The book evaluates the case for deliberate development of automated public transportation and mobility-as-a-service as paths towards sustainable mobility, describing critical approaches to the planning and management of vehicle automation technology. It serves as a reference for understanding the full life cycle of the multi-year transportation systems planning processes, including novel regulation, planning, and acquisition tools for regional transportation.
Application-oriented, research-based, and solution-oriented rather than predict-and-warn, The End of Driving concludes with a detailed discussion of the systems design needed for accomplishing this shift.
From the Foreword by Susan Shaheen: The authors ... extend potential solutions through a set of open-ended exercises after each chapter. Their approach is both strategic and deliberate. They lead the reader from definitions and context setting to the transition toward automation, employing a range of creative strategies and policies. While our quest to understand how to deploy automated vehicles is just beginning, this book provides a thoughtful introduction to inform this evolution.
Offers a workable public transit solution design melding the traditional "acquire-and-operate" mode with the absorption of new technology
Provides a step-by-step discussion of digital systems designs and effective regulation-by-data approaches needed for a new urban mobility
Learning aids include case study scenarios, chapter objectives and discussion questions, sidebars and a glossary
Автор: Xinyu Zhang Название: Multi-sensor Fusion for Autonomous Driving ISBN: 9819932793 ISBN-13(EAN): 9789819932795 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Although sensor fusion is an essential prerequisite for autonomous driving, it entails a number of challenges and potential risks. For example, the commonly used deep fusion networks are lacking in interpretability and robustness. To address these fundamental issues, this book introduces the mechanism of deep fusion models from the perspective of uncertainty and models the initial risks in order to create a robust fusion architecture. This book reviews the multi-sensor data fusion methods applied in autonomous driving, and the main body is divided into three parts: Basic, Method, and Advance. Starting from the mechanism of data fusion, it comprehensively reviews the development of automatic perception technology and data fusion technology, and gives a comprehensive overview of various perception tasks based on multimodal data fusion. The book then proposes a series of innovative algorithms for various autonomous driving perception tasks, to effectively improve the accuracy and robustness of autonomous driving-related tasks, and provide ideas for solving the challenges in multi-sensor fusion methods. Furthermore, to transition from technical research to intelligent connected collaboration applications, it proposes a series of exploratory contents such as practical fusion datasets, vehicle-road collaboration, and fusion mechanisms. In contrast to the existing literature on data fusion and autonomous driving, this book focuses more on the deep fusion method for perception-related tasks, emphasizes the theoretical explanation of the fusion method, and fully considers the relevant scenarios in engineering practice. Helping readers acquire an in-depth understanding of fusion methods and theories in autonomous driving, it can be used as a textbook for graduate students and scholars in related fields or as a reference guide for engineers who wish to apply deep fusion methods.
Автор: Sjafrie, Hanky (sgec, Munich, Germany) Название: Introduction to self-driving vehicle technology ISBN: 0367321254 ISBN-13(EAN): 9780367321253 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book aims to teach the core concepts that make Self-driving vehicles (SDVs) possible. It is aimed at people who want to get their teeth into self-driving vehicle technology, by providing genuine technical insights where other books just skim the surface.
Автор: Ren Название: Autonomous driving algorithms and Its IC Design ISBN: 9819928966 ISBN-13(EAN): 9789819928965 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too “power-hungry,” which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2–6 focus on algorithm design for perception and planning control. Chapters 7–10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.
Автор: Fingscheidt Название: Deep Neural Networks and Data for Automated Driving ISBN: 3031012321 ISBN-13(EAN): 9783031012327 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Gupta Nishu, Prakash Arun, Tripathi Rajeev Название: Internet of Vehicles and its Applications in Autonomous Driving ISBN: 3030463370 ISBN-13(EAN): 9783030463373 Издательство: Springer Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides an insight on the importance that Internet of Vehicles (IoV) solutions can have in taking care of vehicular safety through internetworking and automation.
This concise reference to driving simulators conveys the technology behind simulator systems used to test driver assistance systems and automated vehicles, including EV. Based on three examples, the technology is covered, including architecture and computer graphics. Several parameters for evaluation of Human Machine Interface are detailed, and application examples provided.
Автор: Wei, Wei (guangzhou University, China) Название: Tof lidar for autonomous driving ISBN: 0750337214 ISBN-13(EAN): 9780750337212 Издательство: Неизвестно Рейтинг: Цена: 39313.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Torchinsky, Jason Название: Robot, take the wheel ISBN: 1948062976 ISBN-13(EAN): 9781948062978 Издательство: Неизвестно Рейтинг: Цена: 2344.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: From the star of the YouTube sensation Jason Drives, the senior editor of the acclaimed website Jalopnik, and a producer of Jay Leno`s Garage comes the wittiest and most insightful guide yet to self-driving cars and the road ahead. Self-driving cars sound fantastical and futuristic and yet they`ll soon be on every str
Автор: Hang, Peng (nanyang Technological University, Singapore) Lv, Chen (nanyang Technological University, Singapore) Chen, Xinbo (tongji University, China) Название: Human-like decision making and control for autonomous driving ISBN: 1032262087 ISBN-13(EAN): 9781032262086 Издательство: Taylor&Francis Рейтинг: Цена: 13014.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.
Описание: This book deals with the estimation of travel time in a very comprehensive and exhaustive way. Travel time information is and will continue to be one key indicator of the quality of service of a road network and a highly valued knowledge for drivers. Moreover, travel times are key inputs for comprehensive traffic management systems. All the above-mentioned aspects are covered in this book. The first chapters expound on the different types of travel time information that traffic management centers work with, their estimation, their utility and their dissemination. They also remark those aspects in which this information should be improved, especially considering future cooperative driving environments. Next, the book introduces and validates two new methodologies designed to improve current travel time information systems, which additionally have a high degree of applicability: since they use data from widely disseminated sources, they could be immediately implemented by many administrations without the need for large investments. Finally, travel times are addressed in the context of dynamic traffic management systems. The evolution of these systems in parallel with technological and communication advancements is thoroughly discussed. Special attention is paid to data analytics and models, including data-driven approaches, aimed at understanding and predicting travel patterns in urban scenarios. Additionally, the role of dynamic origin-to-destination matrices in these schemes is analyzed in detail.
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