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 Название: Computing Systems for Autonomous Driving
Автор: Weisong Shi, Liangkai Liu
Издательство: Springer
Год: 2021
Страниц: 239
Язык: английский
Формат: pdf (true), epub
Размер: 26.2 MB
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.
In the last 5 years, with the vast improvements in computing technologies, e.g., sensors, computer vision, machine learning, and hardware acceleration, and the wide deployment of communication mechanisms, e.g., dedicated short-range communications (DSRC), cellular vehicle-to-everything (C-V2X), and 5G, autonomous driving techniques have attracted massive attention from both the academic and automotive communities.
To achieve the vision of autonomous driving, determining how to make the vehicle understand the environment correctly and make safe controls in real-time is the essential task. Rich sensors including camera, LiDAR (light detection and ranging), radar, inertial measurement unit (IMU), global navigation satellite system (GNSS), and sonar, as well as powerful computation devices, are installed on the vehicle. This design makes autonomous driving a real powerful “computer on wheels.” In addition to hardware, the rapid development of deep learning algorithms in object/lane detection, simultaneous localization and mapping (SLAM), and vehicle control also promotes the real deployment and prototyping of autonomous vehicles. The autonomous vehicle’s computing systems are defined to cover everything (excluding the vehicle’s mechanical parts), including sensors, computation, communication, storage, power management, and full-stack software. Plenty of algorithms and systems are designed to process sensor data and make a reliable decision in real-time.
This book can be used by senior undergraduate students and graduate students in engineering and computer science majors. We hope this book will serve as a reference and a starting point for those who are interested in working in this field.
Comtents:
1. Autonomous Driving Landscape
2. Computing Framework for Autonomous Driving
3. Algorithm Deployment Optimization
4. Systems Runtime Optimization
5. Dataset and Benchmark
6. Autonomous Driving Simulators
7. Hardware Platforms
8. Smart Infrastructure for Autonomous Driving
9. Challenges and Open Problems
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