Creating Autonomous Vehicle Systems, Second Edition de Shaoshan Liu

Creating Autonomous Vehicle Systems, Second Edition: Synthesis Lectures on Computer Science

Autor Shaoshan Liu, Liyun Li, Jie Tang, Shuang Wu, Jean-Luc Gaudiot

en Limba Engleză Paperback – 11 sep 2020

În industria vehiculelor autonome, complexitatea derivă din necesitatea de a integra armonios algoritmi de inteligență artificială cu infrastructuri hardware și cloud robuste. Creating Autonomous Vehicle Systems, Second Edition reprezintă o resursă tehnică esențială care demistifică acest ecosistem, oferind o privire de ansamblu asupra întregului ansamblu tehnologic. Remarcăm modul în care autorii reușesc să segmenteze arhitectura vehiculului în trei piloni fundamentali: algoritmii de percepție și control, sistemele client (precum ROS și platformele hardware) și platforma cloud, vitală pentru stocarea datelor și antrenarea modelelor de deep learning.

Ca și Weisong Shi în Computing Systems for Autonomous Driving, autorii distilează experiență reală în principii acționabile, punând un accent deosebit pe cerințele de fiabilitate și procesare în timp real. Structura cărții este riguros organizată, ghidând cititorul de la fundamentele localizării și percepției până la tehnici avansate de învățare prin întărire pentru luarea deciziilor. Față de prima ediție, această versiune actualizată introduce studii de caz comerciale extrem de utile, precum vehiculele pentru livrări în medii urbane complexe, oferind o punte necesară între teorie și implementarea în teren.

Credem că valoarea adăugată a acestei lucrări rezidă în abordarea holistică. Nu se limitează doar la software, ci analizează și infrastructura de suport, reflectând expertiza autorului Shaoshan Liu din lucrări precum Robotic Computing on FPGAs. Este o resursă care echilibrează rigoarea academică cu pragmatismul ingineresc, fiind potrivită atât pentru mediul universitar, cât și pentru practicienii care dezvoltă soluții de mobilitate autonomă de nivel 4 și 5.

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Specificații

ISBN-13: 9783031006777
ISBN-10: 3031006771
Pagini: 221
Ilustrații: XXIII, 221 p.
Dimensiuni: 191 x 235 x 17 mm
Greutate: 0.43 kg
Ediția:2
Editura: Springer International Publishing
Colecția Springer
Seria Synthesis Lectures on Computer Science

Locul publicării:Cham, Switzerland

De ce să citești această carte

Această carte este ideală pentru inginerii și studenții care doresc să înțeleagă arhitectura completă a unui vehicul autonom, dincolo de simplele linii de cod. Cititorul câștigă o perspectivă clară asupra modului în care hardware-ul, sistemul de operare robotic și cloud-ul colaborează pentru a asigura siguranța în trafic. Este un ghid practic, ancorat în studii de caz reale, esențial pentru oricine vrea să activeze în domeniul transportului inteligent.

Despre autor

Shaoshan Liu este un expert recunoscut în sisteme autonome și robotică, având o experiență vastă în arhitecturi de calcul performante. Lucrările sale anterioare, precum Network and Parallel Computing și Robotic Computing on FPGAs, demonstrează un interes profund pentru optimizarea hardware și procesarea paralelă, elemente critice pentru vehiculele autonome moderne. Alături de o echipă de coautori din mediul academic și industrial, precum Jean-Luc Gaudiot, Liu contribuie la standardizarea educației în domeniul conducerii autonome prin publicații tehnice de referință în seria Synthesis Lectures on Computer Science.

Descriere scurtă

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.

Cuprins

Preface to the Second Edition.- Teaching and Learning from This Book.- Introduction to Autonomous Driving.- Autonomous Vehicle Localization.- Perception in Autonomous Driving.- Deep Learning in Autonomous Driving Perception.- Prediction and Routing.- Decision, Planning, and Control.- Reinforcement Learning-Based Planning and Control.- Client Systems for Autonomous Driving.- Cloud Platform for Autonomous Driving.- Autonomous Last-Mile Delivery Vehicles in Complex Traffic Environments.- PerceptIn's Autonomous Vehicles Lite.- Author Biographies.

Notă biografică

Dr. Shaoshan Liu is chairman and co-founder of PerceptIn. He attended UC Irvine for his undergraduate and graduate studies and obtained a Ph.D. in Computer Engineering in 2010. His research focuses on Computer Architecture, Big Data Platforms, Deep Learning Infrastructure, and Robotics. He has over eight years of industry experience: before founding PerceptIn, he was with Baidu USA, where he led the Autonomous Driving Systems team. Before joining Baidu USA, he worked on Big Data platforms at LinkedIn, Operating Systems kernel at Microsoft, Reconfigurable Computing at Microsoft Research, GPU Computing at INRIA (France), Runtime Systems at Intel Research, and Hardware at Broadcom.

Dr. Liyun Li has more than 6 years of experience in autonomous driving software development. He is currently a principal engineer and manager at Xpeng Motors (NYSE: XPEV), where he leads the software development of Navigation Guided Pilot (NGP). Before joining Xpeng Motors, he served as a principal engineerat JD.com. He is one of the founding members of Baidu USA’s autonomous driving team, where he has driven and led the effort of building core modules in Baidu’s open-source autonomous driving system, including planning and prediction. Dr. Li has published two books in Autonomous Driving: Creating Autonomous Vehicle Systems (Morgan & Claypool Publishers) and The First Technology Book in Autonomous Driving (Publishing House of Electronics Industry (PHEI)). He is also the inventor of more than 20 international patents in autonomous driving. Dr. Li received his Ph.D. in Computer Science from New York University and Bachelor’s degree in Electronic Engineering from Tsinghua University.

Dr. Jie Tang is currently an associate professor in the School of Computer Science and Engineering of South China University of Technology, Guangzhou, China. Before joining SCUT, Dr. Tang was a post-doctoral researcher at the University of California, Riverside and Clarkson University from December 2013 to August 2015. She received the B.E. from the University of Defense Technology in 2006, and the Ph.D. degree from the Beijing Institute of Technology in 2012, both in Computer Science. From 2009-2011, she was a visiting researcher at the PArallel Systems and Computer Architecture Lab at the University of California, Irvine, USA.

Dr. Shuang Wu is currently a scientist at Yitu Inc. Previously senior research scientist at Baidu's AI lab in Sunnyvale, CA, senior architect at Baidu USDC. He earned his Ph.D. in Physics from University of Southern California, and was a postdoctoral researcher at UCLA. He has conducted research in computer and biological vision, applied machine learning in industry for computational advertisement, and speech recognition. He has published in conferences such as NIPS, ICML etc.

Dr. Jean-Luc Gaudiot received the Diplome d'Ingenieur from ESIEE, Paris, France in 1976 and M.S. and Ph.D. degrees in Computer Science from UCLA in 1977 and 1982,respectively. He is currently a professor in the Electrical Engineering and Computer Science Department at UC, Irvine. Prior to joining UCI in 2002, he was Professor of Electrical Engineering at the University of Southern California since 1982. His research interests include multithreaded architectures, fault-tolerant multiprocessors, and implementation of reconfigurable architectures. He has published over 250 journal and conference papers. His research has been sponsored by NSF, DoE, and DARPA, as well as a number of industrial companies. He has served the community in various positions and was just elected to the presidency of the IEEE Computer Society for 2017.