Autonomous Driving Perception

Fundamentals and Applications
2023
ISBN: 978-981-99-4289-3
Verlag: Springer

Buch, Englisch, 387 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 604 g

Reihe: Advances in Computer Vision and Pattern Recognition

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.

Zielgruppe

Research

Autoren/Hrsg.

Fan, Rui

Guo, Sicen

Bocus, Mohammud Junaid

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Chapter 1: Key Ingredients of Self-Driving Cars.- Chapter 2: Advanced Sensors for Next-Generation Autonomous Vehicles.- Chapter 3: Recent Advances in Multi-Camera and Camera-LIDAR Calibration.- Chapter 4: Deep Optical Flow for Autonomous Driving: A Review.- Chapter 5: Computer Stereo Vision for Autonomous Driving Perpection: From Explicit Programming to Deep Learning.- Chapter 6: Deep Monocular Depth Estimation for Autonomous Driving.- 

Über Autor(innen)

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.