Weisong Shi

This book offers an accessible introduction to the fast-moving world of autonomous driving. Aimed at students, researchers, and professionals, it provides both a comprehensive overview and a hands-on guide to the core concepts and practical tools used to build self-driving cars. Autonomous driving stands at the intersection of artificial intelligence, robotics, embedded systems, and transportation engineering. Over the past two decades, the field has advanced from speculative prototypes to road-tested systems with significant industrial and societal impact. This textbook reflects that evolution, offering readers a structured yet flexible entry point into the autonomous vehicle (AV) ecosystem. Key topics include how autonomous vehicles perceive their surroundings, determine their location, plan routes, and make driving decisions. Each chapter bridges foundational concepts with real-world applications using open-source platforms such as ROS2, CARLA, BlueICE, and Autoware. Universe. Readers will gain hands-on experience through simulation environments, real-world datasets, and modular development tools. A unique emphasis on experiential learning encourages active engagement with the complexities of AV development. From simulating sensor fusion to implementing planning strategies and security protocols, learners interact directly with the technical and design challenges inherent in the field. Reflection exercises throughout the book emphasize ethical considerations and the societal implications of AV technology—underscoring the importance of responsible innovation alongside technical fluency. While some background in programming and mathematics is helpful, the content is designed to be approachable and engaging for a broad audience interested in the future of mobility. The organization of the text—from foundational chapters on perception and localization to advanced discussions of full-stack systems and industry trends—mirrors the layered architecture of an actual autonomous vehicle. Introduction to Autonomous Driving equips readers not only with the skills needed to contribute to AV projects today, but also with the conceptual clarity and critical perspective required for leadership in this transformative domain.

Understand the computing technology that will power a connected future The explosive growth of the Internet of Things (IoT) in recent years has revolutionized virtually every area of technology. It has also driven a drastically increased demand for computing power, as traditional cloud computing proved insufficient in terms of bandwidth, latency, and privacy. Edge computing, in which data is processed at the edge of the network, closer to where it’s generated, has emerged as an alternative which meets the new data needs of an increasingly connected world. Edge Computing offers a thorough but accessible overview of this cutting-edge technology. Beginning with the fundamentals of edge computing, including its history, key characteristics, and use cases, it describes the architecture and infrastructure of edge computing and the hardware that enables it. The book also explores edge intelligence, where artificial intelligence is integrated into edge computing to enable smaller, faster, and more autonomous decision-making. The result is an essential tool for any researcher looking to understand this increasingly ubiquitous method for processing data. Edge Computing readers will also find: Real-world applications and case studies drawn from industries including healthcare and urban developmentDetailed discussion of topics including latency, security, privacy, and scalabilityA concluding summary of key findings and a look forward at an evolving computing landscape Edge Computing is ideal for students, professionals, and enthusiasts looking to understand one of technology’s most exciting new paradigms.

Over the past century, vehicles have predominantly functioned as a means of transportation. However, as vehicular computation and communication capacities continue to expand, it is anticipated that upcoming connected vehicle (CVs) will not only serve their conventional transport functions but also act as versatile mobile computing platforms. This book presents the concept of Vehicle Computing, encompassing five primary functionalities of CVs: computation, communication, energy management, sensing, and data storage. It proposes a potential business model and explores the challenges and opportunities associated with these domains. Vehicle Computing serves as an important resource for the research community and practitioners in the field of edge computing and cyber physical system, capturing the essence of a rapidly changing industry, addressing the challenges and opportunities associated with connected vehicles (including software-defined vehicles, autonomous vehicles, electric vehicles), machine learning, communication, sensing, data storage, energy management, and computer systems. It synthesizes the latest research and real-world applications, offering valuable insights to both academia and industry professionals. Vehicle Computing covers topics such as: The fundamentals of Vehicle Computing, including its historical context and key components. Advanced communication and networking technologies for connected vehicles. Sensing and data acquisition techniques, including edge and cloud computing integration. Energy management and storage, focusing on electric vehicle infrastructure and vehicle-to-grid. Data storage and processing strategies for vehicular environments. Business models, opportunities, and challenges associated with Vehicle Computing. Real-world applications and case studies, highlighting best practices and future trends.

Over the past century, vehicles have predominantly functioned as a means of transportation. However, as vehicular computation and communication capacities continue to expand, it is anticipated that upcoming connected vehicle (CVs) will not only serve their conventional transport functions but also act as versatile mobile computing platforms. This book presents the concept of Vehicle Computing, encompassing five primary functionalities of CVs: computation, communication, energy management, sensing, and data storage. It proposes a potential business model and explores the challenges and opportunities associated with these domains. Vehicle Computing serves as an important resource for the research community and practitioners in the field of edge computing and cyber physical system, capturing the essence of a rapidly changing industry, addressing the challenges and opportunities associated with connected vehicles (including software-defined vehicles, autonomous vehicles, electric vehicles), machine learning, communication, sensing, data storage, energy management, and computer systems. It synthesizes the latest research and real-world applications, offering valuable insights to both academia and industry professionals. Vehicle Computing covers topics such as: The fundamentals of Vehicle Computing, including its historical context and key components. Advanced communication and networking technologies for connected vehicles. Sensing and data acquisition techniques, including edge and cloud computing integration. Energy management and storage, focusing on electric vehicle infrastructure and vehicle-to-grid. Data storage and processing strategies for vehicular environments. Business models, opportunities, and challenges associated with Vehicle Computing. Real-world applications and case studies, highlighting best practices and future trends.

This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving. Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies. This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving. Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies.

This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving. Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies. This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving. Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies.

The success of the Internet of Things and rich cloud services have helped create the need for edge computing, in which data processing occurs in part at the network edge, rather than completely in the cloud. In Edge Computing: A Primer the vision and definition of Edge computing is introduced, as well as several key techniques that enable Edge computing. Then, four applications that benefit from Edge computing are presented as case studies, ranging from smart homes and public safety to medical services, followed by a discussion of several open challenges and opportunities in Edge computing. Finally, several key tools for edge computing such as virtualization and resource management are explained.