| 000 -LEADER |
|---|
| fixed length control field |
09087nam a2200769 i 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
8093778 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
IEEE |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20200413152926.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
171025s2018 caua foab 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781681730080 |
| Qualifying information |
ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781681731674 |
| Qualifying information |
epub |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781681730073 |
| Qualifying information |
print |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.2200/S00787ED1V01Y201707CSL009 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(CaBNVSL)swl00407897 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)1007548148 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CaBNVSL |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
CaBNVSL |
| Modifying agency |
CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
TL152.8 |
| Item number |
.L583 2018 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
629.23 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Liu, Shaoshan, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Creating autonomous vehicle systems / |
| Statement of responsibility, etc. |
Shaoshan Liu, Liyun Li, Jie Tang, Shuang Wu, Jean-Luc Gaudiot. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
[San Rafael, California] : |
| Name of producer, publisher, distributor, manufacturer |
Morgan & Claypool, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2018. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 PDF (x, 186 pages) : |
| Other physical details |
illustrations. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
electronic |
| Source |
isbdmedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on computer science, |
| International Standard Serial Number |
1932-1686 ; |
| Volume/sequential designation |
# 9 |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
System requirements: Adobe Acrobat Reader. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Part of: Synthesis digital library of engineering and computer science. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. Introduction to autonomous driving -- 1.1 Autonomous driving technologies overview -- 1.2 Autonomous driving algorithms -- 1.2.1 Sensing -- 1.2.2 Perception -- 1.2.3 Object recognition and tracking -- 1.2.4 Action -- 1.3 Autonomous driving client system -- 1.3.1 Robot operating system (ROS) -- 1.3.2 Hardware platform -- 1.4 Autonomous driving cloud platform -- 1.4.1 Simulation -- 1.4.2 HD map production -- 1.4.3 Deep learning model training -- 1.5 It is just the beginning -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
2. Autonomous vehicle localization -- 2.1 Localization with GNSS -- 2.1.1 GNSS overview -- 2.1.2 GNSS error analysis -- 2.1.3 Satellite-based augmentation systems -- 2.1.4 Real-time kinematic and differential GPS -- 2.1.5 Precise point positioning -- 2.1.6 GNSS INS integration -- 2.2 Localization with LiDAR and high-definition maps -- 2.2.1 LiDAR overview -- 2.2.2 High-definition maps overview -- 2.2.3 Localization with LiDAR and HD map -- 2.3 Visual odometry -- 2.3.1 Stereo visual odometry -- 2.3.2 Monocular visual odometry -- 2.3.3 Visual inertial odometry -- 2.4 Dead reckoning and wheel odometry -- 2.4.1 Wheel encoders -- 2.4.2 Wheel odometry errors -- 2.4.3 Reduction of wheel odometry errors -- 2.5 Sensor fusion -- 2.5.1 CMU Boss for urban challenge -- 2.5.2 Stanford Junior for urban challenge -- 2.5.3 Bertha from Mercedes Benz -- 2.6 References -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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 -- 3.5.1 Stereo and depth -- 3.5.2 Optical flow -- 3.5.3 Scene flow -- 3.6 Tracking -- 3.7 Conclusions -- 3.8 References -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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 -- 4.4.1 Stereo -- 4.4.2 Optical flow -- 4.5 Conclusion -- 4.6 References -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
5. Prediction and routing -- 5.1 Planning and control overview -- 5.1.1 Architecture: planning and control in a broader sense -- 5.1.2 Scope of each module: solve the problem with modules -- 5.2 Traffic prediction -- 5.2.1 Behavior prediction as classification -- 5.2.2 Vehicle trajectory generation -- 5.3 Lane level routing -- 5.3.1 Constructing a weighted directed graph for routing -- 5.3.2 Typical routing algorithms -- 5.3.3 Routing graph cost: weak or strong routing -- 5.4 Conclusions -- 5.5 References -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
6. Decision, planning, and control -- 6.1 Behavioral decisions -- 6.1.1 Markov decision process approach -- 6.1.2 Scenario-based divide and conquer approach -- 6.2 Motion planning -- 6.2.1 Vehicle model, road model, and SL-coordination system -- 6.2.2 Motion planning with path planning and speed planning -- 6.2.3 Motion planning with longitudinal planning and lateral planning -- 6.3 Feedback control -- 6.3.1 Bicycle model -- 6.3.2 PID control -- 6.4 Conclusions -- 6.5 References -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
7. Reinforcement learning-based planning and control -- 7.1 Introduction -- 7.2 Reinforcement learning -- 7.2.1 Q-learning -- 7.2.2 Actor-critic methods -- 7.3 Learning-based planning and control in autonomous driving -- 7.3.1 Reinforcement learning on behavioral decision -- 7.3.2 Reinforcement learning on planning and control -- 7.4 Conclusions -- 7.5 References -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
8. Client systems for autonomous driving -- 8.1 Autonomous driving: a complex system -- 8.2 Operating system for autonomous driving -- 8.2.1 ROS overview -- 8.2.2 System reliability -- 8.2.3 Performance improvement -- 8.2.4 Resource management and security -- 8.3 Computing platform -- 8.3.1 Computing platform implementation -- 8.3.2 Existing computing solutions -- 8.3.3 Computer architecture design exploration -- 8.4 References -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
9. Cloud platform for autonomous driving -- 9.1 Introduction -- 9.2 Infrastructure -- 9.2.1 Distributed computing framework -- 9.2.2 Distributed storage -- 9.2.3 Heterogeneous computing -- 9.3 Simulation -- 9.3.1 BinPipeRDD -- 9.3.2 Connecting Spark and ROS -- 9.3.3 Performance -- 9.4 Model training -- 9.4.1 Why use Spark? -- 9.4.2 Training platform architecture -- 9.4.3 Heterogeneous computing -- 9.5 HD map generation -- 9.5.1 HD map -- 9.5.2 Map generation in the cloud -- 9.6 Conclusions -- 9.7 References -- Author biographies. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Compendex |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
INSPEC |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google scholar |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google book search |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating 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 about its 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, we are able to test new algorithms and update the HD map--plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. 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 plenty of references for an effective, deeper exploration of the various technologies. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Also available in print. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Title from PDF title page (viewed on October 25, 2017). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Autonomous vehicles |
| General subdivision |
Data processing. |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
autonomous driving |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
driverless cars |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
perception |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
vehicle localization |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
planning and control |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
autonomous driving hardware platform |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
autonomous driving cloud infrastructures |
| 655 #0 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Li, Liyun, |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tang, Jie, |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Wu, Shuang, |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gaudiot, Jean-Luc, |
| Relator term |
author. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| International Standard Book Number |
9781681730073 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis digital library of engineering and computer science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures on computer science ; |
| Volume/sequential designation |
# 9. |
| International Standard Serial Number |
1932-1686 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to resource |
| Uniform Resource Identifier |
http://ieeexplore.ieee.org/servlet/opac?bknumber=8093778 |
