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

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Autonomous Driving Vehicle Detection Methods under Different Low-Light Scenes

Authors
Jiacheng Fan1, *
1School of Electronic Engineering, Xidian University, Xi’an, Shaanxi, China
*Corresponding author. Email: 22022100066@stu.xidian.edu.cn
Corresponding Author
Jiacheng Fan
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_18How to use a DOI?
Keywords
Low-light scenes; Autonomous driving; Vehicle detection; Generative adversarial networks (GAN); You Only Look Once (YOLO)
Abstract

Vehicle detection is a core task in environmental perception for autonomous driving, and its performance directly affects driving safety. However, low-light scenes, due to issues such as low light levels, atmospheric scattering, and sensor occlusion, result in image distortion, difficulty in edge detection, and impaired signal transmission. These problems seriously undermine the accuracy and robustness of traditional detection methods, thus emerging as a critical bottleneck in environmental perception for autonomous driving. This paper presents a review of autonomous vehicle detection under low-light scenes, with a focus on the impacts of three typical low-light scenes—nighttime, foggy, and rainy weather—on visual perception, as well as the targeted technical solutions for vehicle detection in these scenes. It combs through existing technical frameworks and provides a systematic organization and introduction. In addition, this paper introduces mainstream vehicle detection datasets, identifies current research bottlenecks, and discusses future development directions. The aim of this paper is to comprehensively understand the influence of low-light scenes on vehicle detection, provide effective references for autonomous driving vehicle detection methods under different low-light scenes, and lay a foundation for the future development and practical application of autonomous driving technology.

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_18How 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  - Jiacheng Fan
PY  - 2026
DA  - 2026/04/24
TI  - Autonomous Driving Vehicle Detection Methods under Different Low-Light Scenes
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 154
EP  - 163
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_18
DO  - 10.2991/978-94-6239-648-7_18
ID  - Fan2026
ER  -