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REVIEW article

Front. Phys.
Sec. Radiation Detectors and Imaging
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1485026
This article is part of the Research Topic Advanced Deep Learning Algorithms for Multi-Source Data and Imaging View all 8 articles

Towards Full Autonomous Driving: Challenges and Frontiers

Provisionally accepted
Wei He Wei He 1Wenhe Chen Wenhe Chen 2,3*Siyi Tian Siyi Tian 4Lunning Zhang Lunning Zhang 5
  • 1 Fudan University, Shanghai, Shanghai Municipality, China
  • 2 Jiangsu University of Technology, Changzhou, Jiangsu, China
  • 3 Shanghai Huace Navigation Technology Ltd., Shanghai, China
  • 4 Shanghai Jiao Tong University, Shanghai, Shanghai Municipality, China
  • 5 Shanghai Future Space-Time Technology Ltd, Shanghai, China

The final, formatted version of the article will be published soon.

    With the rapid advancement of information technology and intelligent systems, autonomous driving has garnered significant attention and research in recent years. Key technologies, such as Simultaneous Localization and Mapping (SLAM), Perception and Localization, and Scene Segmentation, have proven to be essential in this field. These technologies not only evolve independently, each with its own research focus and application paths, but also complement and rely on one another in various complex autonomous driving scenarios. This paper provides a comprehensive review of the development and current state of these technologies, along with a forecast of their future trends.

    Keywords: Autonomous Driving, Simultaneous localization and mapping, Perception and Localization, scene segmentation, deep learning

    Received: 23 Aug 2024; Accepted: 02 Oct 2024.

    Copyright: © 2024 He, Chen, Tian and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wenhe Chen, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.