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Autonomous Driving Perception

Fundamentals and Applications

  • Book
  • © 2023

Overview

Editors:
  1. Rui Fan

    1. Control Science & Engineering, Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China

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  2. Sicen Guo

    1. Control Science & Engineering, Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China

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    You can also search for this editor in PubMed   Google Scholar

  3. Mohammud Junaid Bocus

    1. Electrical & Electronic Engineering, University of Bristol, Bristol, UK

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  • Discover the ultimate guide that takes you through the most recent breakthroughs in computer vision
  • This comprehensive book goes beyond the basics, immersing you in the world of machine vision and intelligence
  • Delve into the depths of cutting-edge perception algorithms, as we provide in-depth and state-of-the-art reviews

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. In-Sensor Visual Devices for Perception and Inference

    • Yanan Liu, Hepeng Ni, Chao Yuwen, Xinyu Yang, Yuhang Ming, Huixin Zhong et al.
    Pages 1-35

  3. Environmental Perception Using Fish-Eye Cameras for Autonomous Driving

    • Yeqiang Qian, Ming Yang, John M. Dolan
    Pages 37-61

  4. Stereo Matching: Fundamentals, State-of-the-Art, and Existing Challenges

    • Chuang-Wei Liu, Hengli Wang, Sicen Guo, Mohammud Junaid Bocus, Qijun Chen, Rui Fan
    Pages 63-100

  5. Semantic Segmentation for Autonomous Driving

    • Jingwei Yang, Sicen Guo, Mohammud Junaid Bocus, Qijun Chen, Rui Fan
    Pages 101-137

  6. 3D Object Detection in Autonomous Driving

    • Peng Yun, Yuxuan Liu, Xiaoyang Yan, Jiahang Li, Jiachen Wang, Lei Tai et al.
    Pages 139-173

  7. Collaborative 3D Object Detection

    • Siheng Chen, Yue Hu
    Pages 175-204

  8. Enabling Robust SLAM for Mobile Robots with Sensor Fusion

    • Jianhao Jiao, Xiangcheng Hu, Xupeng Xie, Jin Wu, Hexiang Wei, Lu Fan et al.
    Pages 205-239

  9. Visual SLAM for Texture-Less Environment

    • Yanchao Dong, Yuhao Liu, Sixiong Xu
    Pages 241-279

  10. Multi-task Perception for Autonomous Driving

    • Xiaodan Liang, Xiwen Liang, Hang Xu
    Pages 281-321

  11. Bird’s Eye View Perception for Autonomous Driving

    • Jiayuan Du, Shuai Su, Rui Fan, Qijun Chen
    Pages 323-356

  12. Road Environment Perception for Safe and Comfortable Driving

    • Sicen Guo, Yu Jiang, Jiahang Li, Dacheng Zhou, Shuai Su, Mohammud Junaid Bocus et al.
    Pages 357-387
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Keywords

About this book

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.

Editors and Affiliations

  • Control Science & Engineering, Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China

    Rui Fan, Sicen Guo

  • Electrical & Electronic Engineering, University of Bristol, Bristol, UK

    Mohammud Junaid Bocus

About the editors

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.



Bibliographic Information

  • Book Title: Autonomous Driving Perception

  • Book Subtitle: Fundamentals and Applications

  • Editors: Rui Fan, Sicen Guo, Mohammud Junaid Bocus

  • Series Title: Advances in Computer Vision and Pattern Recognition

  • DOI: https://doi.org/10.1007/978-981-99-4287-9

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023

  • Hardcover ISBN: 978-981-99-4286-2Published: 07 October 2023

  • Softcover ISBN: 978-981-99-4289-3Due: 07 November 2023

  • eBook ISBN: 978-981-99-4287-9Published: 06 October 2023

  • Series ISSN: 2191-6586

  • Series E-ISSN: 2191-6594

  • Edition Number: 1

  • Number of Pages: X, 387

  • Number of Illustrations: 12 b/w illustrations, 161 illustrations in colour

  • Topics: Robotics, Machine Learning, Image Processing and Computer Vision

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