Autonomous Driving Perception
Springer Verlag, Singapore
978-981-99-4286-2 (ISBN)
Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.
Prof. Rui (Ranger) Fan received the B.Eng. degree in Automation from the Harbin Institute of Technology in 2015 and the Ph.D. degree in Electrical and Electronic Engineering from the University of Bristol in 2018. He worked as a Research Associate at the Hong Kong University of Science and Technology from 2018 to 2020 and a Postdoc Scholar-Empolyee at the University of California San Diego between 2020 and 2021. He is currently a (full) Professor at Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems. Rui was named in Stanford University List of Top 2% Scientists Worldwide in 2022. His research interests include computer vision, deep learning, and robotics. Miss Sicen Guo is currently pursuing her Ph.D. degree, supervised by Prof. Rui Fan, with the Machine Intelligence and Autonomous Systems (MIAS) Group at Tongji University. She won 2 championships in the VEX Robotics competitions: the Silk Road Robot Innovation Competition and the AsianYouth Robotics Competition. She also won the national first prize and the Zigbee Innovation Award of the HUAWEI CUP National Undergraduate IoT design contest. She participated in the 2020 Undergraduate Electronic design contest and was honored with the national second prize. Her research interests include stereo matching and semantic segmentation. Dr. Mohammud Junaid Bocus is an exceptional researcher in the field of electronic and communication engineering. With an illustrious academic background, including a B.Eng. degree (first-class honors) in Electronic and Communication Engineering from the University of Mauritius and an M.Sc. degree (distinction) in Wireless Communications and Signal Processing from the esteemed University of Bristol, he solidified his expertise by earning a Ph.D. degree in Electrical and Electronic Engineering from the same institution. Driven by a passion for innovation, Dr. Bocus explores diverse domains such as terrestrial and underwater wireless communication, signal processing, video coding, computer vision, and machine/deep learning. His research endeavors focus on practical applications, notably road surface reconstruction, lane detection, and road crack/pothole detection, leveraging state-of-the-art techniques in computer vision and machine learning. With prior experience as a research associate on the OPERA project, he also made significant contributions in passive human activity recognition and localization using radio-frequency systems. Currently, as a postdoctoral researcher at the University of Bristol, Dr. Bocus plays a pivotal role in the NGCDI project, delving into the fascinating realm of Goal Oriented Communications, joint communications and sensing, and deep learning with emergent communications.
Chapter 1: Key Ingredients of Self-Driving Cars: Rui Fan, Mohammud Junaid Bocus, and Ioannis Pitas
Chapter 2: Advanced Sensors for Next-Generation Autonomous Vehicles: Yanan Liu and Walterio Mayol-Cuevas
Chapter 3: Recent Advances in Multi-Camera and Camera-LIDAR Calibration: Jianhao Jiao, Jin Wu, Ming Liu
Chapter 4: Deep Optical Flow for Autonomous Driving: A Review: Yixin Fei, Zhongkai Zhao, Sicen Guo
Chapter 5: Computer Stereo Vision for Autonomous Driving Perpection: From Explicit Programming to Deep Learning: Rui Fan, Hengli Wang, Sicen Guo
Chapter 6: Deep Monocular Depth Estimation for Autonomous Driving: Mengjiao Shen and Qijun Chen
Chapter 7: Deep 2D/3D Object Detection and Tracking: A State-of-the-Art Review: Peng Yun, Lei Tai, Ming Liu
Chapter 8: A Survey of State-of-the-Art Simultaneous Localization and Mapping Systems: Jianhao Jiao, Yilong Zhu, Ming Liu
Chapter 9: Generative Adversarial Domain Adaptation for Autonomous Driving under Chanllenging Environmental Conditions: Nachuan Ma, Bing Liu, Mohammud Junaid Bocus
Chapter 10: Multiple Lane Detection for Autononous Driving: Umar Ozgunalp
Chapter 11: Human Pose Estimation in Real-World Driving Settings: Romain Guesdon, Carlos Crispim-Junior, Laure Tougne
Chapter 12: Single-Modal and Data-Fusion Semantic Driving Scene Segmentation: Jingwei Yang, Yi Feng
Chapter 13: Road Defect and Anomaly Detection for Safe and Comfortable Autonomous Driving: Sicen Guo, Rui Fan, Li Wang, Mohammud Junaid Bocus
Chapter 14: Urban Digital Transformation for Intelligent Vehicles: Jianfeng Lu
| Erscheinungsdatum | 11.10.2023 |
|---|---|
| Reihe/Serie | Advances in Computer Vision and Pattern Recognition |
| Zusatzinfo | 161 Illustrations, color; 12 Illustrations, black and white; X, 387 p. 173 illus., 161 illus. in color. |
| Verlagsort | Singapore |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Autonomous Driving • computer vision • Deep learning • Image Processing • machine learning • Robotics |
| ISBN-10 | 981-99-4286-1 / 9819942861 |
| ISBN-13 | 978-981-99-4286-2 / 9789819942862 |
| Zustand | Neuware |
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