Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)

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Autonomous Vehicle Detection and Classification using Feature Mapping in Generative Adversarial Network

Authors
G. Balamurugan1, *, R. Kaviarasan2, R. Kalaiyarasan3
1Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, TamilNadu, India
2Department of CSE (Cyber Security), RGM College of Engineering and Technology (A), Nandyal, Andhra Pradesh, India
3Department of ECE, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India
*Corresponding author. Email: balamurg1@srmist.edu.in
Corresponding Author
G. Balamurugan
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-616-6_26How to use a DOI?
Keywords
Autonomous Vehicles; Boundary Detection; Computer Vision; Feature Extraction; GAN
Abstract

Autonomous vehicle (AV) navigation is facilitated using LiDAR and imaging sensors for detecting objects and identifying vehicles. In particular, computer vision (CV) and automated processing are required to maximize navigation through image processing. The problem of vehicle detection and classification is influenced by the extracted feature mapping due to uneven image sizes. This article introduces a feature-mapping-based vehicle boundary detection method for addressing this problem. The proposed method uses a generative adversarial network for mapping extracted features based on contrast. Using multiple mapping instances, the low intensity and high contrast features are used to detect vehicle boundaries. Based on the detected boundaries, the adversarial network mapping rate is trained to improve the detection precision. The proposed method improves accuracy by 9.42%, precision by 10.38%, and reduces the mean square error by 9.28% for the maximum boundaries identified.

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 Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 March 2026
ISBN
978-94-6239-616-6
ISSN
1951-6851
DOI
10.2991/978-94-6239-616-6_26How 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  - G. Balamurugan
AU  - R. Kaviarasan
AU  - R. Kalaiyarasan
PY  - 2026
DA  - 2026/03/31
TI  - Autonomous Vehicle Detection and Classification using Feature Mapping in Generative Adversarial Network
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 307
EP  - 318
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_26
DO  - 10.2991/978-94-6239-616-6_26
ID  - Balamurugan2026
ER  -