A Literature Review on AI-Based Traffic Sign Recognition Systems

• Author(s): Shivangi Gupta; Priyanka Pandey; Trupti Hubli; Impana S
• Paper ID: 1712930
• Page: 1374-1375
• Published Date: 18-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 6 December-2025

Abstract

Traffic Sign Recognition (TSR) is a fundamental component of intelligent transportation systems and Advanced Driver Assistance Systems (ADAS). Accurate detection and classification of traffic signs enable safer driving, reduce human error, and support autonomous vehicle decision-making. Recent advancements in artificial intelligence, particularly deep learning and computer vision, have significantly improved TSR performance. Techniques such as Convolutional Neural Networks (CNNs), attention-based architectures, hybrid classifiers, and lightweight transformer models enable real-time recognition even on embedded platforms. However, real-world deployment still faces challenges including environmental variability, occlusion, sign degradation, regional diversity, and computational constraints. This manuscript provides a detailed review of recent AI-based TSR approaches, analyzes their advantages and limitations, identifies research gaps, and outlines future directions toward robust, scalable, and real-time TSR systems.

Keywords

Traffic Sign Recognition, Artificial Intelligence, Deep Learning, ADAS, Computer Vision, Autonomous Vehicles

Citations

IRE Journals:
Shivangi Gupta, Priyanka Pandey, Trupti Hubli, Impana S "A Literature Review on AI-Based Traffic Sign Recognition Systems" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 1374-1375 https://doi.org/10.64388/IREV9I6-1712930

IEEE:
Shivangi Gupta, Priyanka Pandey, Trupti Hubli, Impana S "A Literature Review on AI-Based Traffic Sign Recognition Systems" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712930