Creating Autonomous Vehicle Systems

Dr. Shaoshan Liu is the Founder and CEO of PerceptIn, an autonomous driving technology company. Since founding, PerceptIn has attracted over 12 million USD of funding of from top-notch venture capital firms, including Walden International, Matrix Partners, and Samsung Ventures. Before founding PerceptIn, Dr. Shaoshan Liu had over 10 years of experience at leading R&D institutes, including Baidu USA, LinkedIn, Microsoft, Microsoft Research, INRIA, Intel Research, and Broadcom. Dr. Shaoshan Liu received a Ph.D. in Computer Engineering from the University of California, Irvine. He has published over 60 high-quality research papers and holds over 150 U.S. international patents on robotics and autonomous driving and is also the lead author of the best-selling textbooks Creating Autonomous Vehicle Systems and Engineering Autonomous Vehicles and Robots. Dr. Shaoshan Liu is a senior member of IEEE, a distinguished speaker of the IEEE Computer Society, and a distinguished speaker of the ACM. Dr. Shaoshan Liu is also the founder of the IEEE Special Technical Community on Autonomous Driving Technologies. 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 engineer at 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 postdoctoral researcher at the University of California, Riverside and Clarkson University from December 2013 to August 2015. She received a B.E. from the University of Defense Technology in 2006, and a Ph.D. 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 (PASCAL) at the University of California, Irvine, USA. Dr. Shuang Wu is currently a scientist at Yitu Technology. Previously he was a senior research scientist at Baidu's AI lab in Sunnyvale, CA and a 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 and ICML. 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 (UCI). 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 communi

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