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.
Dr. Weisong Shi is the Alumni Distinguished Professor and Chair of the Department of Computer and Information Sciences at the University of Delaware. He directs the Connected and Autonomous Research (CAR) Laboratory, which actively collaborates with leading companies in the automotive industry. A globally recognized leader in edge computing and autonomous driving, Dr. Shi currently serves as the Honorary Director of the NSF eCAT Industry University Cooperative Research Center (IUCRC). His landmark paper, “Edge Computing: Vision and Challenges,” has garnered over 8,800 citations. He is the Editor-in-Chief of IEEE Internet Computing and the founding chair of the steering committees for the ACM/IEEE Symposium on Edge Computing (SEC) and the IEEE International Conference on Mobility (MOST). Dr. Shi is a Fellow of the IEEE.
Yuankai He is a Ph.D. candidate in Computer and Information Sciences at the University of Delaware and a researcher at the Connected and Autonomous Research Laboratory (CAR Lab). His work focuses on simulation frameworks, digital twin technologies, vehicle-to- everything (V2X) communication, and infrastructure-integrated computing systems for autonomous vehicles. He has developed modular tools for high-fidelity testing environments and contributed to systems that connect vehicles and infrastructure for collaborative driving.
