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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.
This book presents the Proceedings from the Second International Workshop GLOW 2024 held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2024, in Jeju Island, South Korea, in August 2024. The 11 full papers and 4 short papers included in this book were carefully reviewed and selected from 22 submissions. They were organized in topical sections as follows: efficient methods for low-resource hardware; efficient fintuning with limited data; advancements in multimodal systems; recognition and reasoning in the open world.
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
This book constitutes the proceedings of the First International Conference on Intelligent Robotics and Manufacturing, IRAM 2012, held in Kuala Lumpur, Malaysia, in November 2012. The 64 revised full papers included in this volume were carefully reviewed and selected from 102 initial submissions. The papers are organized in topical sections named: mobile robots, intelligent autonomous systems, robot vision and robust, autonomous agents, micro, meso and nano-scale automation and assembly, flexible manufacturing systems, CIM and micro-machining, and fabrication techniques.
"Immerse yourself in the evolving world of automotive technology with ADAS and Automated Driving - Systems Engineering. Explore advanced driver assistance systems (ADAS) and automated driving, revealing the automotive industry’s technological revolution. As technology becomes a driving force, this book serves as a guide to understanding cutting-edge technologies deployed by leading vehicle manufacturers. Discover how multiple systems synergize to provide ADAS and automated driving functions. Authored by an industry expert, this book explores systems engineering’s crucial role in designing, safety-critical cyber-physical systems. Gain practical insights into the processes and methods adapted for the current technological era of software-defined vehicles, influenced by AI, digitalization, and rapid technological advances. Whether you're a seasoned engineer navigating the shift to software-defined vehicles or a student eager to grasp systems engineering methods, this book is your key to unlocking the skills demanded in the exciting era of digitalization. Immerse yourself in real-world examples drawn from industry experiences, bridging the gap between theory and practical application. Gain the knowledge and expertise needed to embark on projects involving the intricate world of cyber-physical systems with ADAS and Automated Driving - Systems Engineering. “As this book demonstrates, systems engineering is needed more than ever to navigate the complexities of the type of projects where alternative delivery models are applied and to help ensure effective delivery even within the constraints of aggressive and adaptable schedules.” Dr David Ward Global Head of Vehicle Resilience—Functional Safety HORIBA MIRA Limited “This book holistically explains the lifecycle and the processes for ADAS and autonomous systems and their influence on the overall vehicle over its complete lifecycle.” Matthias Schulze Vice President, ADAS Product, ecarx" (ISBN 9781468607444, ISBN 9781468607451, ISBN 9781468607468, DOI 10.4271/9781468607451)