Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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Analysis of Visual Navigation Systems in Autonomous Driving

Authors
Yizhan Zhang1, *
1School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, 230009, China
*Corresponding author. Email: 2023213033@mail.hfut.edu.cn
Corresponding Author
Yizhan Zhang
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_21How to use a DOI?
Keywords
Visual navigation; Autonomous driving; Environmental perception; Multi-sensor fusion
Abstract

As a key technology for environmental perception and precise positioning in autonomous driving, the performance of visual navigation systems directly impacts vehicle safety and efficiency in complex environments. This system primarily relies on visual cameras to capture rich road scene information, utilizing image processing and deep learning algorithms to identify lane markings, traffic signs, pedestrians, and obstacles. It offers advantages such as low cost and broad information dimensions. However, its performance is susceptible to interference from lighting variations, adverse weather, and dynamic scenes, leading to reduced recognition reliability and insufficient positioning accuracy. To enhance system robustness, current research focuses on multi-sensor fusion techniques. By integrating the ranging stability of millimeter-wave radar or the 3D spatial perception capabilities of lidar, these approaches compensate for the limitations of single-visual sensors. This paper systematically outlines the core principles and technical architecture of visual navigation systems, analyzes two major challenges—adaptability and real-time performance—in complex environments, explores solutions through algorithm optimization and hardware collaboration, and examines high-precision semantic mapping and end-to-end learning for autonomous driving. It aims to provide theoretical support for the research and engineering implementation of visual navigation systems in autonomous driving.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_21How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yizhan Zhang
PY  - 2026
DA  - 2026/04/24
TI  - Analysis of Visual Navigation Systems in Autonomous Driving
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 186
EP  - 195
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_21
DO  - 10.2991/978-94-6239-648-7_21
ID  - Zhang2026
ER  -