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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

TRAFFIC SIGN RECOGNITION FOR AUTONOMOUS DRIVING APPLICATIONS

B Prasanna Adithya Kumar Jha SR Manideep S Likhitha

Shaik Nagur Vali

Department of CSE (Data Science) ACE Engineering College

Hyderabad Telangana India
DOI:https://doi.org/10.55041/ijcope.v2i4.269

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Traffic Sign Recognition (TSR) is a key component of autonomous driving systems, enabling vehicles to interpret regulatory, warning, and informational signs for safe and informed navigation. This work presents a deep-learning-based TSR approach that uses convolutional neural networks to extract meaningful features and classify traffic signs accurately under varying lighting, weather, and occlusion conditions. The model is trained on a diverse dataset and supported by preprocessing and optimization techniques to enhance robustness and generalization. Designed for real-time operation, the proposed system integrates efficiently with autonomous vehicle perception modules, contributing to reliable decision- making and improved road safety within intelligent transportation environments.

How to Cite this Paper

Prasanna, B., Jha, A. K., Manideep, S. & Likhitha, S. (2026). Traffic Sign Recognition for Autonomous Driving Applications. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.269

Prasanna, B, et al.. "Traffic Sign Recognition for Autonomous Driving Applications." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.269.

Prasanna, B,Adithya Jha,SR Manideep, and S Likhitha. "Traffic Sign Recognition for Autonomous Driving Applications." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.269.

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References


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  5. Saad, M., Khan, Q., & Rehman, S. (2021). Robust Traffic Sign Classification Using Transfer Learning Models. International Journal of Advanced Computer Science and Applications (IJACSA), 12(4), 223–230.

  6. Wang, Y., Chen, J., & Hu, F. (2022). Hybrid Deep Learning Model for Real-World Traffic Sign Recognition. Sensors, 22(18), 6781.

  7. Ahmed, S., Paul, S., & Rahman, M. (2023). Efficient Vision-Based Traffic Sign Recognition for Intelligent Vehicles. Applied Sciences, 13(2), 1125.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Apr 11 2026
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