| Description |
1 online resource (xxxi, 216 pages) : illustrations (some color) |
| Series |
Synthesis lectures on computer science, 1932-1686 ; #12 |
|
Synthesis lectures on computer science ; #12.
|
| Contents |
1. Introduction to autonomous driving -- 1.1. Autonomous driving technologies overview -- 1.2. Autonomous driving algorithms -- 1.3. Autonomous driving client system -- 1.4. Autonomous driving cloud platform -- 1.5. It is just the beginning |
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2. Autonomous vehicle localization -- 2.1. Localization with GNSS -- 2.2. Localization with LiDAR and high-definition maps -- 2.3. Visual odometry -- 2.4. Dead reckoning and wheel odometry -- 2.5. Sensor fusion -- 2.6. References |
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3. Perception in autonomous driving -- 3.1. Introduction -- 3.2. Datasets -- 3.3. Detection -- 3.4. Segmentation -- 3.5. Stereo, optical flow, and scene flow -- 3.6. Tracking -- 3.7. Conclusion -- 3.8. References |
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4. Deep learning in autonomous driving perception -- 4.1. Convolutional neural networks -- 4.2. Detection -- 4.3. Semantic segmentation -- 4.4. Stereo and optical flow -- 4.5. Conclusion -- 4.6. References |
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5. Prediction and routing -- 5.1. Planning and control overview -- 5.2. Traffic prediction -- 5.3. Lane level routing -- 5.4. Conclusions -- 5.5. References |
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6. Decision, planning, and control -- 6.1. Behavioral decisions -- 6.2. Motion planning -- 6.3. Feedback control -- 6.4. Conclusions -- 6.5. References |
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7. Reinforcement learning-based planning and control -- 7.1. Introduction -- 7.2. Reinforcement learning -- 7.3. Learning-based planning and control in autonomous driving -- 7.4. Conclusions -- 7.5. References |
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8. Client systems for autonomous driving -- 8.1. Autonomous driving : a complex system -- 8.2. Operating system for autonomous driving -- 8.3. Computing platform -- 8.4. References |
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9. Cloud platform for autonomous driving -- 9.1. Introduction -- 9.2. Infrastructure -- 9.3. Simulation -- 9.4. Model training -- 9.5. HD map generation -- 9.6. Conclusions -- 9.7. References |
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10. Autonomous last-mile delivery vehicles in complex traffic environments -- 10.1. Background and motivations -- 10.2. Autonomous delivery technologies in complex traffic conditions -- 10.3. Jd.com : an autonomous driving solution -- 10.4. Safety and security strategies -- 10.5. Production deployments -- 10.6. Lessons learned -- 10.7. References |
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11. Perceptin's autonomous vehicles lite -- 11.1. Introduction -- 11.2. Expensive autonomous driving technologies -- 11.3. Achieving affordability and reliability -- 11.4. Deploying autonomous LSEV for mobility as a service -- 11.5. Conclusions -- 11.6. References |
| Summary |
This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions as to its future actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, new algorithms can be tested so as to update the HD map--in addition to training better recognition, tracking, and decision models. Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled "Teaching and Learning from this Book" was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find extensive references for an effective, deeper exploration of the various technologies |
| Analysis |
autonomous driving |
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driverless cars |
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perception |
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vehicle localization |
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planning and control |
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autonomous driving hardware platform |
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autonomous driving cloud infrastructures |
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low-speed autonomous vehicle |
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autonomous last-mile delivery vehicle |
| Bibliography |
Includes bibliographical references |
| Notes |
Title from PDF title page (viewed on October 12, 2020) |
| Subject |
Automated vehicles -- Design and construction
|
| Form |
Electronic book
|
| Author |
Li, Liyun (Computer scientist), author.
|
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Tang, Jie (College teacher), author.
|
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Wu, Shuang (Research scientist), author.
|
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Gaudiot, Jean-Luc, author.
|
| ISBN |
9781681739366 |
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1681739364 |
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9781681739380 |
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1681739380 |
|
9781681739373 |
|
1681739372 |
|
9783031018053 |
|
3031018052 |
|
